Ekramul Islam
Verified Expert in Engineering
Machine Learning Engineer and Developer
Dhaka, Dhaka Division, Bangladesh
Toptal member since November 19, 2025
Ekramul is an accomplished machine learning (ML) engineer with experience at a Fortune 500 company. He specializes in developing complex AI agents with LangGraph and deploying open-source large language models (LLMs) with low latency. Ekramul's research yields state-of-the-art results in NLP tasks, including shallow parsing, sentiment analysis, named entity recognition, and more, resulting in publications at top-tier conferences such as KDD, NAACL, and EMNLP.
Portfolio
Experience
- Python - 5 years
- Scikit-learn - 5 years
- PyTorch - 4 years
- Hugging Face Transformers - 4 years
- FastAPI - 3 years
- TensorFlow - 3 years
- LangGraph - 1 year
- ChromaDB - 1 year
Preferred Environment
Python, PyTorch, LangGraph, FastAPI, TensorFlow, Scikit-learn, Hugging Face Transformers, ChromaDB, Amazon SageMaker, Docker
The most amazing...
...thing I've developed is a shallow and deep parsing pipeline that achieved state-of-the-art results, with a 96.47 F-score on the Penn Treebank.
Work Experience
Machine Learning Engineer
IQVIA
- Developed a conversational AI agent with LangGraph tool-calling capabilities to automate complex ML workflows, dynamically clustering datasets, and allowing users to select features and filter datasets. I received the Impact Award for it.
- Deployed open-source LLMs, including DeepSeek and Qwen, on AWS SageMaker DJL Serving with vLLM back ends, achieving first-token latency as low as 90ms.
- Collaborated with cross-functional teams to develop an AI agent using LangGraph, where the agent parses user queries, generates SQL queries, retrieves data, and creates final answers with visualizations.
Machine Learning Engineer
Giga-tech
- Developed data preparation, training, and inference pipelines with FastAPI and PyPI package for shallow parsing, achieving state-of-the-art results with a 96.47 F-score on the Penn Treebank.
- Conducted transformer-based model experimentation, cross-dataset analysis, and statistical significance tests for sentiment analysis, leading to a publication as the first author at KDD 2023.
- Achieved state-of-the-art results in named entity recognition on all Bangla datasets, published in NAACL 2025.
- Developed a Bangla lemmatizer with 98.17% accuracy, published in EMNLP 2023.
Experience
The Brief News
KEY FEATURES
• Built a semantic deduplication engine to identify same news stories across multiple news sources, utilizing Gemini to generate unified and unbiased summaries.
• Developed an intelligent news assistant using LangGraph and vector databases to perform agentic RAG, allowing users to query the corpus for deep analytical insights.
• Engineered automated visualization tools within the assistant to extract data-driven trends and generate real-time charts from the news database.
Handwritten Character Image Generation
Bangla Next Sequence Prediction
https://ieeexplore.ieee.org/document/9333518Proposed a solution using a Trie and a combination of LSTM and N-gram to predict the relevant next sequence list in Bangla, published in IEE (ICAICT '20 ).
Education
Bachelor of Science Degree in Computer Science and Engineering
Shahjalal University of Science and Technology - Sylhet, Bangladesh
Certifications
Neural Networks and Deep Learning
Coursera
Skills
Libraries/APIs
PyTorch, TensorFlow, Scikit-learn, Pandas, Matplotlib, Claude API, Hugging Face Transformers, OpenAI API, REST APIs, Pydantic, NumPy
Tools
Claude, Git, ChatGPT, Claude Agent SDK, GitHub, Amazon SageMaker
Languages
Python, SQL, Snowflake
Frameworks
Agentic Frameworks, LangGraph
Platforms
Jupyter Notebook, Docker, LangSmith, Amazon Web Services (AWS), Google Cloud Platform (GCP)
Storage
JSON, Data Pipelines, MongoDB, Amazon S3 (AWS S3), Elasticsearch
Paradigms
Microservices, Object-oriented Programming (OOP)
Other
FastAPI, Machine Learning, Deep Learning, Natural Language Processing (NLP), LangChain, Amazon Bedrock, AI Agents, Large Language Models (LLMs), Artificial Intelligence (AI), Retrieval-augmented Generation (RAG), Agentic AI, Generative Artificial Intelligence (GenAI), Prompt Engineering, Deep Neural Networks (DNNs), Hugging Face, Data Science, Anthropic, AI Chatbots, AI Development, Model Deployment, Agentic RAG Systems, Machine Learning Operations (MLOps), Deployment, Open-source LLMs, Chatbots, Local Hosting, Chatbot Conversation Design, AI Assistants, AI Modeling, AI Integration, ChatGPT API, ChatGPT Prompts, AI Pipeline, Large Language Model Operations (LLMOps), Qdrant, Transformers, Data Quality, Cursor AI, ML Pipelines, ChromaDB, Generative Adversarial Networks (GANs), Research, APIs, Fine-tuning, Small Language Models (SLMs), Vector Databases, Architecture, Product Development, Cloud Services, Containers, Vector Stores, Data Protection, OpenAI, GPU Computing, MLflow, Image Generation, Human-in-the-loop (HITL), Text to Image, RAG Architecture, RAG Pipelines, Computer Vision, Software Development Lifecycle (SDLC), Digital Signal Processing, Long Short-term Memory (LSTM), N-gram Language Models
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