Ashish is available for hire
Hire AshishAshish Shrivastava
Verified Expert in Engineering
Artificial Intelligence Developer
Bangalore, India
Toptal member since October 14, 2026
Bio
Ashish has spent 12 years building deep learning and large language model solutions across technology and finance. His toolkit centers on AWS Fargate, Spark, and DynamoDB. While at TIFIN, Ashish reduced LLM inference costs by 70% and improved latency to under 3 seconds.
Portfolio
Netskope
Agentic AI, Large Language Models (LLMs), Retrieval-augmented Generation (RAG)...
TIFIN GROUP
Large Language Models (LLMs), Agentic AI, AI Agent Orchestration, Algorithms...
Walmart
Large Language Models (LLMs), Agentic AI, AI Agent Orchestration...
Experience
- Deep Learning - 12 years
- Evaluation - 12 years
- Natural Language Processing (NLP) - 12 years
- Large Language Models (LLMs) - 8 years
- Inference Optimization - 5 years
- Retrieval-augmented Generation (RAG) - 3 years
- Multi-agent Systems - 3 years
- Agentic AI - 3 years
Work Experience
Senior Staff Engineer
2026 - PRESENT
Netskope
- Slashed overall API consumption costs by 40%, drastically minimized TTFT latency to deliver near-instantaneous agent execution, and established a scalable, cost-predictive foundation for onboarding concurrent enterprise agents.
- Designed production-grade Agentic AI architectures incorporating LLM reasoning, tool use, orchestration, retrieval, memory, and autonomous workflows.
- Built enterprise-scale retrieval-augmented generation (RAG) pipelines using vector databases, semantic search, knowledge graphs, and contextual retrieval.
- Developed AI system evaluation and observability approaches that incorporate LLM tracing, OpenTelemetry, and Langfuse.
Technologies: Agentic AI, Large Language Models (LLMs), Retrieval-augmented Generation (RAG), OpenTelemetry, Langfuse, Multi-agent Systems, LangSmith, LangChain, LangGraph, Amazon DynamoDB, AWS Fargate, AI Agent Orchestration, Prompt Optimization, Semantic Caching, Artificial Intelligence (AI), Database Management Systems (DBMS), Deep Learning, Foundation Models, Evaluation, Inference Optimization, Knowledge Graphs, LangFuse, Machine Learning, Machine Learning Operations (MLOps), Algorithms, Natural Language Processing (NLP), Production Deployment, Python, Production ML Systems, Transformers, Semantic Search, PyTorch, Data Structures, Prompt Engineering, Text Classification, Classification, Harness, Claude, AI Prompts, ChatGPT, AI Workflow, ChatGPT Prompts, API Development, Claude API, Data Extraction, Data Pipelines, ETL, Reporting, Reports, Training, Integration, Scripting, Claude Code, Anthropic, PostgreSQL, Full-stack Development, Full-stack
Principal AI Research Lead
2025 - 2026
TIFIN GROUP
- Designed and implemented multimodal RAG pipelines integrating text and vision Transformer models for structured document understanding.
- Reduced LLM inference cost by 70% while improving latency to under 3 seconds P95 and maintaining or improving response quality.
- Led applied AI research focused on LLM reasoning reliability, post-training optimization, model quality, and hallucination reduction.
- Designed and implemented comprehensive LLM evaluation frameworks measuring answer faithfulness, retrieval accuracy, reasoning quality, and task-specific performance.
Technologies: Large Language Models (LLMs), Agentic AI, AI Agent Orchestration, Algorithms, Amazon DynamoDB, Artificial Intelligence (AI), AWS Fargate, Database Management Systems (DBMS), Databases, Deep Learning, Foundation Models, Evaluation, Inference Optimization, Knowledge Graphs, LangChain, LangFuse, Hugging Face Transformers, LangGraph, Machine Learning, Multi-agent Systems, LangSmith, Machine Learning Operations (MLOps), OpenTelemetry, Production Deployment, Prompt Optimization, Python, Production ML Systems, PyTorch, Retrieval-augmented Generation (RAG), Semantic Caching, Transformers, Semantic Search, Data Structures, vLLM, Prompt Engineering, Embeddings from Language Models (ELMo), Text Classification, Classification, Harness, Claude, AI Prompts, ChatGPT, AI Workflow, ChatGPT Prompts, API Development, Claude API, Data Extraction, Data Pipelines, ETL, Mentorship, Reporting, Reports, Team Mentoring, Training, Integration, Claude Code, Anthropic, PostgreSQL, Speech Recognition, Text-to-Speech (TTS), Full-stack Development, Full-stack
Senior Data Scientist
2022 - 2025
Walmart
- Built and deployed large-scale deep learning and machine learning systems delivering 6.5+ million predictions per day.
- Developed LLM-based pipelines for product attribute extraction from product images and descriptions.
- Implemented distributed model training and evaluation pipelines for production ML workloads.
- Collaborated with cross-functional engineering and product teams to deploy production-grade AI or ML solutions at enterprise scale.
Technologies: Large Language Models (LLMs), Agentic AI, AI Agent Orchestration, Artificial Intelligence (AI), Database Management Systems (DBMS), Amazon DynamoDB, AWS Fargate, Machine Learning, Deep Learning, Foundation Models, Evaluation, Fine-tuning, LLM Fine-tuning, Supervised Fine-tuning (SFT), Inference Optimization, vLLM, Prompt Engineering, Knowledge Graphs, Algorithms, Hugging Face Transformers, Transformers, Production Deployment, Semantic Search, PyTorch, Data Structures, Convolutional Neural Networks (CNNs), Embeddings from Language Models (ELMo), Computer Vision, Text Classification, Classification, AI Prompts, ChatGPT, ChatGPT Prompts, API Development, Data Extraction, Data Pipelines, ETL, Mentorship, Reporting, Reports, Team Mentoring, Training, Integration, Scripting
Senior Machine Learning Engineer
2021 - 2022
Coinbase
- Designed and implemented NLP-based classification and ranking systems for business-critical decision systems.
- Built scalable machine learning solutions supporting production decision-making workflows.
- Applied deep learning and NLP methodologies to improve model performance and system reliability.
Technologies: Natural Language Processing (NLP), Algorithms, Amazon DynamoDB, Artificial Intelligence (AI), Machine Learning, Deep Learning, Database Management Systems (DBMS), Hugging Face Transformers, Supervised Fine-tuning (SFT), Keyword Analysis, Transformers, Evaluation, Production Deployment, Semantic Search, PyTorch, Data Structures, LLM Fine-tuning, Embeddings from Language Models (ELMo), Text Classification, Classification, Named-entity Recognition (NER), Keyword Research, Data Extraction, Data Pipelines, ETL, Reporting, Reports, Training, Integration
R&D Engineer, NLP & Conversational AI
2016 - 2021
IPsoft
- Designed dialogue systems and data augmentation frameworks, including NL-Augmenter.
- Built evaluation mechanisms for conversational robustness, contextual coherence, and model quality.
- Conducted R&D in deep learning, NLP, conversational AI, natural language understanding, and dialogue management.
- Researched methods for improving conversational AI performance through data augmentation, evaluation, and model experimentation.
Technologies: Natural Language Processing (NLP), Artificial Intelligence (AI), Machine Learning, Deep Learning, Transformers, Hugging Face Transformers, LSTM, Long Short-term Memory (LSTM), Gated Recurrent Unit (GRU), Neural Networks, Siamese Neural Networks, Convolutional Neural Networks (CNNs), Word Embedding, Embeddings from Language Models (ELMo), Algorithms, Computer Vision, Evaluation, Production Deployment, Semantic Search, Knowledge Graphs, PyTorch, Data Structures, LLM Fine-tuning, Text Classification, Classification, Named-entity Recognition (NER), Keyword Research, Sentiment Analysis, Data Extraction, Data Pipelines, ETL, Mentorship, Reporting, Reports, Team Mentoring, Training, Integration, Scripting, Speech Recognition, Text-to-Speech (TTS)
Application Developer, ML & NLP
2015 - 2016
Intel
- Developed ML-based document understanding systems, reducing manual effort by 40%.
- Built NLP pipelines for information extraction, document processing, and automated understanding.
- Developed machine learning workflows for extracting structured information from unstructured documents.
Technologies: Machine Learning Operations (MLOps), Natural Language Processing (NLP), Machine Learning, Deep Learning, Word Embedding, Keyword Analysis, Text Classification, Classification, Named-entity Recognition (NER), Python, Evaluation, Production Deployment, Semantic Search, Data Structures, LSTM, Long Short-term Memory (LSTM), Gated Recurrent Unit (GRU), Neural Networks, Keyword Research, Sentiment Analysis, Data Extraction, Training
Associate, ML & NLP
2014 - 2015
Cognizant
- Built automated data extraction and parsing pipelines using machine learning and NLP.
- Developed scalable data-processing workflows for enterprise applications.
- Developed machine learning workflows for extracting structured information from unstructured documents.
Technologies: Natural Language Processing (NLP), Machine Learning, Deep Learning, Python, Artificial Intelligence (AI), Keyword Research, Keyword Analysis, Sentiment Analysis, Word Embedding, Evaluation, Production Deployment, Data Structures, LSTM, Long Short-term Memory (LSTM), Gated Recurrent Unit (GRU), Neural Networks, Text Classification, Classification, Named-entity Recognition (NER), Data Extraction, Training
Project History
Agentic AI for Autonomous Network Diagnostics & Root Cause Analysis
At Netskope, a cloud security company, I designed a production-grade agentic AI system that autonomously diagnoses enterprise network issues, performs root-cause analysis, and recommends remediation, work that previously depended on manual investigation by support engineers.
As the AI architect, I designed the agent architecture end to end: LLM reasoning and planning, tool use over network telemetry and diagnostic APIs, multi-step orchestration, retrieval over product and incident knowledge, and memory so agents could carry context across steps.
The main engineering challenge was running many concurrent enterprise agents at predictable cost and latency. I optimized prompt and context construction, reduced redundant LLM calls, and restructured orchestration, which reduced LLM API consumption costs by 40% and substantially reduced time-to-first-token (TTFT). I also built tracing and evaluation with OpenTelemetry and Langfuse, so every agent decision was observable and debuggable in production.
As the AI architect, I designed the agent architecture end to end: LLM reasoning and planning, tool use over network telemetry and diagnostic APIs, multi-step orchestration, retrieval over product and incident knowledge, and memory so agents could carry context across steps.
The main engineering challenge was running many concurrent enterprise agents at predictable cost and latency. I optimized prompt and context construction, reduced redundant LLM calls, and restructured orchestration, which reduced LLM API consumption costs by 40% and substantially reduced time-to-first-token (TTFT). I also built tracing and evaluation with OpenTelemetry and Langfuse, so every agent decision was observable and debuggable in production.
Multimodal Agentic RAG for Financial Documents: 70% Lower Inference Cost, Under 3s P95
At TIFIN, an AI-first wealth-management fintech, I led the design of a multimodal Agentic RAG system that answers questions over financial documents containing both text and visual content, such as tables, charts, and scanned pages.
As Principal AI Research Lead, I designed a pipeline that integrates text Transformers with vision Transformers, enabling the system to retrieve and reason over visual evidence, not just extracted text. Early versions were accurate but too slow and expensive for production use.
I led a systematic optimization effort across retrieval, context construction, model selection, and inference, benchmarking each change against our evaluation suite so quality never regressed silently. The result: I reduced LLM inference cost by 70% and brought P95 latency under 3 seconds, while maintaining or improving response quality. This turned a research prototype into an economically viable system to run at scale for financial users.
As Principal AI Research Lead, I designed a pipeline that integrates text Transformers with vision Transformers, enabling the system to retrieve and reason over visual evidence, not just extracted text. Early versions were accurate but too slow and expensive for production use.
I led a systematic optimization effort across retrieval, context construction, model selection, and inference, benchmarking each change against our evaluation suite so quality never regressed silently. The result: I reduced LLM inference cost by 70% and brought P95 latency under 3 seconds, while maintaining or improving response quality. This turned a research prototype into an economically viable system to run at scale for financial users.
Enterprise RAG Platform with LLM Evaluation & Observability
At Netskope, I architected an enterprise-scale RAG platform that grounds LLM answers in large volumes of security product documentation, configuration data, and incident knowledge.
I designed the full retrieval stack: document ingestion and chunking, embeddings, vector database indexing, semantic search, knowledge-graph links between related entities, and contextual retrieval so the model receives the most relevant evidence rather than keyword matches.
Because enterprise users need answers they can trust, I built LLM evaluation and observability into the platform from day one. Using OpenTelemetry and Langfuse, every request is traced through retrieval and generation, and retrieval quality, faithfulness, and hallucination are measured continuously. This made quality regressions visible before they reached customers and gave the team a data-driven way to tune retrieval and prompts. The same RAG layer serves as the knowledge backbone for the agentic diagnostics system.
I designed the full retrieval stack: document ingestion and chunking, embeddings, vector database indexing, semantic search, knowledge-graph links between related entities, and contextual retrieval so the model receives the most relevant evidence rather than keyword matches.
Because enterprise users need answers they can trust, I built LLM evaluation and observability into the platform from day one. Using OpenTelemetry and Langfuse, every request is traced through retrieval and generation, and retrieval quality, faithfulness, and hallucination are measured continuously. This made quality regressions visible before they reached customers and gave the team a data-driven way to tune retrieval and prompts. The same RAG layer serves as the knowledge backbone for the agentic diagnostics system.
LLM Evaluation Framework & Hallucination Reduction for Financial AI
At TIFIN, LLM answers were used in a regulated financial domain, where a fluent but wrong answer is worse than no answer. I led applied research on LLM reasoning reliability and hallucination reduction, and designed the evaluation framework that the team used to make model decisions.
The framework measures faithfulness to source documents, retrieval accuracy, reasoning quality, and task-specific performance, combining curated benchmark sets, automated LLM-as-judge scoring, and targeted human review. It lets us compare prompts, retrieval strategies, models, and post-training changes (SFT, LoRA/PEFT) on the same yardstick instead of relying on anecdotal spot checks.
I used it to drive hallucination-reduction and post-training work, and it became the release gate for model and pipeline changes. I also mentored AI/ML engineers and researchers in LLM experimentation, evaluation design, and research methodology, raising the rigor with which the team shipped GenAI features.
The framework measures faithfulness to source documents, retrieval accuracy, reasoning quality, and task-specific performance, combining curated benchmark sets, automated LLM-as-judge scoring, and targeted human review. It lets us compare prompts, retrieval strategies, models, and post-training changes (SFT, LoRA/PEFT) on the same yardstick instead of relying on anecdotal spot checks.
I used it to drive hallucination-reduction and post-training work, and it became the release gate for model and pipeline changes. I also mentored AI/ML engineers and researchers in LLM experimentation, evaluation design, and research methodology, raising the rigor with which the team shipped GenAI features.
Multimodal LLM Product Attribute Extraction for Retail Catalog
At Walmart Global Tech, I developed LLM-based multimodal pipelines that extract structured product attributes (such as color, material, size, brand, and product type) from product images and descriptions across a very large retail catalog.
Product data from sellers is often incomplete or inconsistent, which hurts search, filtering, and recommendations. Text-only extraction missed attributes visible only in images, so I designed pipelines that combine vision and language signals, using LLMs to reason over both and output normalized attribute values.
I owned the work from modeling and evaluation through to production deployment, building evaluation sets to measure extraction accuracy per attribute and deploying the solution at enterprise scale within Walmart's catalog systems. I also mentored ML engineers on deep learning, NLP, and production AI engineering practices.
Product data from sellers is often incomplete or inconsistent, which hurts search, filtering, and recommendations. Text-only extraction missed attributes visible only in images, so I designed pipelines that combine vision and language signals, using LLMs to reason over both and output normalized attribute values.
I owned the work from modeling and evaluation through to production deployment, building evaluation sets to measure extraction accuracy per attribute and deploying the solution at enterprise scale within Walmart's catalog systems. I also mentored ML engineers on deep learning, NLP, and production AI engineering practices.
Production ML Platform Serving 6.5+ Million Predictions per Day
At Walmart Global Tech, I built and deployed large-scale deep learning and ML systems that generate 6.5+ million predictions per day for production retail use cases.
At this scale, the hard problems are as much engineering as modeling. I built distributed training and evaluation pipelines so models could be retrained on large datasets reliably, compared consistently against previous versions, and promoted to production with confidence.
My responsibilities spanned the full lifecycle: data pipelines with Spark, model development in PyTorch and TensorFlow, distributed training, offline evaluation, deployment, and monitoring of production predictions. The result was a dependable, high-throughput prediction system serving 6.5+ million predictions daily, along with reusable training and evaluation infrastructure that other models on the team could adopt.
At this scale, the hard problems are as much engineering as modeling. I built distributed training and evaluation pipelines so models could be retrained on large datasets reliably, compared consistently against previous versions, and promoted to production with confidence.
My responsibilities spanned the full lifecycle: data pipelines with Spark, model development in PyTorch and TensorFlow, distributed training, offline evaluation, deployment, and monitoring of production predictions. The result was a dependable, high-throughput prediction system serving 6.5+ million predictions daily, along with reusable training and evaluation infrastructure that other models on the team could adopt.
NLP Classification & Ranking for Business-critical Decisions
At Coinbase, I designed NLP-based classification and ranking systems that supported business-critical decision workflows, where large volumes of text needed to be categorized and prioritized quickly and accurately.
As a senior machine learning engineer, I owned the modeling and system design, building deep learning NLP models for classification, ranking models to surface the highest-priority items first, and the scalable ML infrastructure to serve them in production decision-making workflows.
I focused on both model performance and system reliability, iterating on model architectures and training data to improve accuracy while making sure the systems behaved predictably under production load. The result was accurate, reliable NLP models running in production and directly supporting business-critical decisions.
As a senior machine learning engineer, I owned the modeling and system design, building deep learning NLP models for classification, ranking models to surface the highest-priority items first, and the scalable ML infrastructure to serve them in production decision-making workflows.
I focused on both model performance and system reliability, iterating on model architectures and training data to improve accuracy while making sure the systems behaved predictably under production load. The result was accurate, reliable NLP models running in production and directly supporting business-critical decisions.
Contextualized Fallback Responses for Enterprise Conversational AI (ACL 2021)
https://aclanthology.org/2021.acl-short.13.pdfAt IPsoft (Amelia), an enterprise conversational AI company, I worked on R&D for NLU and dialogue management in virtual assistants used by large enterprises.
A recurring weakness in production assistants is the fallback: when the bot does not understand, it replies with a generic "Sorry, I didn't get that," which frustrates users and ends conversations. I researched contextualized fallback responses, generating fallback replies that use the conversation context to acknowledge what the user said and keep the dialogue moving. This work was published at ACL 2021.
Alongside this, I designed dialogue systems and built evaluation mechanisms for conversational robustness, contextual coherence, and model quality, so model changes could be measured by how they behave in real multi-turn conversations, not just on single-utterance accuracy. Over five years, I took ideas from research to product, improving how Amelia handled real enterprise conversations.
A recurring weakness in production assistants is the fallback: when the bot does not understand, it replies with a generic "Sorry, I didn't get that," which frustrates users and ends conversations. I researched contextualized fallback responses, generating fallback replies that use the conversation context to acknowledge what the user said and keep the dialogue moving. This work was published at ACL 2021.
Alongside this, I designed dialogue systems and built evaluation mechanisms for conversational robustness, contextual coherence, and model quality, so model changes could be measured by how they behave in real multi-turn conversations, not just on single-utterance accuracy. Over five years, I took ideas from research to product, improving how Amelia handled real enterprise conversations.
NL-Augmenter: Open-Source NLP Data Augmentation Framework (NEJLT 2023)
https://github.com/GEM-benchmark/NL-AugmenterNL-Augmenter is an open-source, participatory framework for task-sensitive natural language data augmentation, published in NEJLT 2023. It provides a large library of transformations and filters used to augment training data and to test how robust NLP models are to realistic variations in language.
During my R&D work at IPsoft, I worked on data augmentation for conversational AI and co-authored NL-Augmenter, contributing transformations and robustness evaluation. Augmentation was directly relevant to our production problem: dialogue and NLU models trained on limited, clean data broke on paraphrases, typos, and phrasing variations that real users produce.
The framework is used by the research community to generate augmented datasets and benchmark model robustness, and the same ideas are fed into how we evaluated and improved the robustness of our conversational AI models.
During my R&D work at IPsoft, I worked on data augmentation for conversational AI and co-authored NL-Augmenter, contributing transformations and robustness evaluation. Augmentation was directly relevant to our production problem: dialogue and NLU models trained on limited, clean data broke on paraphrases, typos, and phrasing variations that real users produce.
The framework is used by the research community to generate augmented datasets and benchmark model robustness, and the same ideas are fed into how we evaluated and improved the robustness of our conversational AI models.
Multimodal Machine Translation (EMNLP WMT 2024)
https://aclanthology.org/2024.wmt-1.79/Research project published at EMNLP WMT 2024 (Conference on Machine Translation) on multimodal machine translation: translating text with the help of an accompanying image.
Text alone is often ambiguous; a word like "bank" or "bat" can only be translated correctly once you know what the image shows. Multimodal MT uses visual context from the image to resolve these ambiguities and produce more accurate translations.
I worked on modeling approaches that combine vision and language representations with Transformer-based translation models, ran experiments on the shared-task benchmarks, and co-authored the paper describing the system and results. This work directly informed my subsequent production work on multimodal pipelines that combine text and vision Transformers.
Text alone is often ambiguous; a word like "bank" or "bat" can only be translated correctly once you know what the image shows. Multimodal MT uses visual context from the image to resolve these ambiguities and produce more accurate translations.
I worked on modeling approaches that combine vision and language representations with Transformer-based translation models, ran experiments on the shared-task benchmarks, and co-authored the paper describing the system and results. This work directly informed my subsequent production work on multimodal pipelines that combine text and vision Transformers.
Multilingual Claim Verification with LLMs (CLEF 2025)
https://ceur-ws.org/Vol-4038/paper_72.pdfResearch project published at CLEF 2025 on multilingual reasoning and claim verification: automatically checking whether a claim is supported or refuted by evidence, across multiple languages.
Fact-checking with LLMs is hard because models must retrieve the right evidence, reason over it, and avoid confidently hallucinating a verdict, and performance often drops outside English.
I worked on LLM-based approaches for evidence retrieval and reasoning-based verification in a multilingual setting, ran experiments on the shared-task benchmarks, and co-authored the paper describing the system and results. The work directly aligns with my production focus on faithfulness, hallucination reduction, and the evaluation of LLM reasoning in RAG systems.
Fact-checking with LLMs is hard because models must retrieve the right evidence, reason over it, and avoid confidently hallucinating a verdict, and performance often drops outside English.
I worked on LLM-based approaches for evidence retrieval and reasoning-based verification in a multilingual setting, ran experiments on the shared-task benchmarks, and co-authored the paper describing the system and results. The work directly aligns with my production focus on faithfulness, hallucination reduction, and the evaluation of LLM reasoning in RAG systems.
Education
2012 - 2014
Master's Degree in Computer Science and Engineering
Indian Institute of Technology, Madras - Chennai, India
2005 - 2009
Bachelor's Degree in Computer Science and Engineering
Gautam Budha Technical University - Lucknow, India
Certifications
SEPTEMBER 2017 - PRESENT
Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization
DeepLearning.AI | via Coursera
SEPTEMBER 2017 - PRESENT
Neural Networks and Deep Learning
DeepLearning.AI | via Coursera
SEPTEMBER 2017 - PRESENT
Structuring Machine Learning Projects
DeepLearning.AI | via Coursera
Skills
Libraries/APIs
Claude API, PyTorch, Hugging Face Transformers, vLLM, LSTM, TensorFlow, API Development
Tools
Claude, AI Prompts, ChatGPT, AWS Fargate, Named-entity Recognition (NER), Claude Code
Languages
Python, Reason ML, Regex
Frameworks
LangGraph, Spark
Paradigms
Foundation Models, Siamese Neural Networks, ETL, Keyword Research
Platforms
Harness, Langfuse, LangSmith
Storage
Databases, Data Pipelines, Amazon DynamoDB, Database Management Systems (DBMS), PostgreSQL
Other
Agentic AI, Large Language Models (LLMs), Retrieval-augmented Generation (RAG), Natural Language Processing (NLP), Deep Learning, Artificial Intelligence (AI), Production ML Systems, Transformers, Evaluation, Inference Optimization, Production Deployment, Machine Learning, Data Structures, Algorithms, LangChain, AI Agent Orchestration, Prompt Optimization, Semantic Caching, Fine-tuning, LLM Fine-tuning, Supervised Fine-tuning (SFT), Prompt Engineering, Neural Networks, Convolutional Neural Networks (CNNs), Word Embedding, Embeddings from Language Models (ELMo), Computer Vision, Text Classification, Classification, Sentiment Analysis, Hyperparameter Optimization, Model Regularization, model optimization, AI Inference, Agentic memory, Multimodal GenAI, AI Evaluation, semantic cache, Vector Databases, Semantic serach, Embedding Models, LLMs Evaluations, LLM Evaluation, AI Hallucinations Management, LoRa, PEFT, Conversational AI, Natural Language Understanding (NLU), Dialog Systems, Text-to-text Transfer Transformer (T5), Data Augmentation, Model Robustness, Machine Translation, Neural Machine Translation, Multilingual NLP, ChatGPT Prompts, Data Extraction, Training, OpenTelemetry, Machine Learning Operations (MLOps), LangFuse, Multi-agent Systems, Semantic Search, Knowledge Graphs, Social Network Analysis, Keyword Analysis, Long Short-term Memory (LSTM), Gated Recurrent Unit (GRU), Information Extraction, distributed training, Open Source, Dependency parsing, Fact Verifivation, AI Workflow, Mentorship, Reporting, Reports, Team Mentoring, Integration, Scripting, Anthropic, Speech Recognition, Text-to-Speech (TTS), Full-stack Development, Full-stack, Software Engineering, Rankings, Multilingual Language Models (MLMs)
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