Kostas is available for hire
Hire KostasKostas Nikolakopoulos
Verified Expert in Engineering
Quant Developer
Athens, Central Athens Regional Unit, Greece
Toptal member since December 18, 2020
Bio
Kostas is a seasoned AI/ML engineer combining deep financial modelling expertise with cutting-edge AI agent development. He has transitioned from leading quant roles at Bank of America and Barclays into building AI-native systems across fintech, insurance, and HR tech. His recent work spans multi-agent orchestration, RAG pipelines, LLM fine-tuning, and cloud deployments. He holds a PhD in theoretical physics from Sussex University and codes in Python, C++, and Go.
Portfolio
Bear Claw LLC
Python, Artificial Intelligence (AI), AI Agents, Agentic AI, AWS Lambda...
RealWear
Artificial Intelligence (AI), Python, LangChain, LangGraph...
GSI Technologies LLC
Artificial Intelligence (AI), Large Language Models (LLMs)...
Experience
- Machine Learning - 10 years
- C++ - 10 years
- Statistical Data Analysis - 10 years
- Python - 10 years
- Statistical Modeling - 10 years
- Mathematical Modeling - 10 years
- Artificial Intelligence (AI) - 10 years
- TensorFlow - 5 years
Preferred Environment
TensorFlow, Jupyter Notebook, RStudio, PyCharm, Visual Studio
The most amazing...
...performance increase I've implemented brought down the time needed to run a model from days to minutes.
Work Experience
AI Agent Engineer
2025 - PRESENT
Bear Claw LLC
- Owned the end-to-end development of the ATS platform’s AI engine, including experimentation, agentic engineering, vector databases, and infrastructure.
- Designed and deployed over 10 AI agents to interface with organizational data and stakeholders, each with distinct roles and tooling; architected a multi-agent orchestration system for dynamic task delegation.
- Built and maintained organization-wide RAG systems, implementing document onboarding and vectorization pipelines, and integrating them into agent workflows to enable knowledge-aware automation.
Technologies: Python, Artificial Intelligence (AI), AI Agents, Agentic AI, AWS Lambda, Data Pipelines, LangChain, APIs, OpenAI Gym, AI Design, Open-source LLMs, Machine Learning, Amazon Simple Queue Service (SQS), Amazon DynamoDB, CrewAI, OpenAI Assistants API, Databricks, Amazon Simple Notification Service (SNS), Agent-oriented Software Engineering (AOSE), Agentic RAG Systems, Vector Databases, Pinecone, FastAPI, Retrieval-augmented Generation (RAG), RAG Architecture, Claude, Claude Code, Multiagent Generative Systems (MAGs), Multi-agent Systems, Multi-agent Orchestration, Conversational AI, Large Language Models (LLMs), LangGraph
AI Developer
2025 - 2026
RealWear
- Developed AI agents capable of interacting with users via speech on smart glasses devices.
- Created multi-agent architectures (orchestrator, swarm, custom) with a tool calling for autonomous interactions with users' connections, emails, and apps.
- Enabled connectivity with various social providers (Google, Microsoft, Git, etc.) for AI agents, and added custom MCP connections with user authentication via Auth0.
- Set up Azure container infrastructure for custom MCP servers.
Technologies: Artificial Intelligence (AI), Python, LangChain, LangGraph, Large Language Models (LLMs), AI Agents, AI Design, Model Context Protocol (MCP), FastAPI, RAG Architecture, Retrieval-augmented Generation (RAG), Claude, Claude Code, Supabase, Auth0, Conversational AI, Multiagent Generative Systems (MAGs), Multi-agent Systems, Multi-agent Orchestration, LLM Agents, Langfuse
AI/ML Lead Engineer
2025 - 2025
GSI Technologies LLC
- Led the AI design and development for the MVP phase of an accounting product. The MVP consisted of a Pinecone DB along with an RAG AI integration for Data Processing.
- Used multiple approaches and techniques to incorporate AI agents into most aspects of the product.
- Integrated AI agents into the back end of the product using AWS tools.
Technologies: Artificial Intelligence (AI), Large Language Models (LLMs), Optical Character Recognition (OCR), Natural Language Processing (NLP), Prompt Engineering, AI Agents, Image Recognition, Document Parsing, Amazon Web Services (AWS), AWS Lambda, Redshift, Pinecone, Amazon Bedrock AgentCore, Vector Databases, Retrieval-augmented Generation (RAG), Python
AI/ML Developer
2025 - 2025
Adjusto, Inc.
- Developed a matching algorithm using a hybrid technique of combining LLMs with traditional machine learning models.
- Achieved both high accuracy and high explainability for the models, which was a regulatory requirement.
- Trained LLMs with the specific task of feature extraction and fed those features to random forest classifiers for training and inference. Optimized the LLMs both for accuracy and cost.
Technologies: Artificial Intelligence (AI), AI Design, Large Language Models (LLMs), Algorithm Design, Data Engineering, Data Pipelines, Vertex AI, Prompt Engineering, OpenAI, Machine Learning, Feature Engineering, Random Forests, XGBoost, Linear Regression, Model Development, Model Evaluation
AI/ML Developer
2024 - 2024
Adjusto, Inc.
- Developed the MVP for the AI library of insurance tech applications. The MVP included an RAG AI agent and vector databases (Pinecone, Qdrant).
- Fine-tuned LLMs to improve the accuracy of the target tasks.
- Developed an evaluation framework for model selection to determine the most accurate model for each task.
Technologies: AI Design, Artificial Intelligence (AI), Large Language Models (LLMs), Algorithm Design, Data Engineering, Learning to Rank (LTR), Google Cloud Platform (GCP), TypeScript, TensorFlow, ML Pipelines, Data Pipelines, Large Language Model Operations (LLMOps), Vertex AI, Prompt Engineering, OpenAI, LangChain, Model Development, Model Evaluation
Trading Systems Quant Developer
2024 - 2024
Emerging Markets Intrinsic LTD
- Developed a Python library to interact with the DAS trader app via HTTP sockets. The library handled messages relating to market data, orders, trades, and positions.
- Developed and iterated stock day trading strategies, refining various strategy flavors to identify optimal performance.
- Created a web-based user interface to interact with the application, allowing users to control program inputs and browse past results and reports.
Technologies: Python, Algorithmic Trading, Stock Trading, Quantitative Finance, Finance, Financial Software, .NET, Trading Systems, Google Cloud Platform (GCP)
LLM Expert (via Toptal)
2024 - 2024
Gynisus Inc.
- Worked on parsing large documents in order to extract reporting rules.
- Tried different LLMs to judge their suitability for the problem.
- Tried different modeling approaches to assess their suitability.
Technologies: Artificial Intelligence (AI), SQL, Large Language Models (LLMs), Databases, PDF, Data Extraction
AI Engineer
2023 - 2024
Adam Aerospace Co., LLC
- Developed an MVP using GPT and publicly available data to optimize its performance. Use a Pinecone DB for data consolidation and processing.
- Experimented with and investigated the functionalities of LLMs and their possible applications to different aspects of the product.
- Maintained the end-to-end infrastructure and monitoring of the AI part of the application.
Technologies: Python, Machine Learning, Natural Language Processing (NLP), Minimum Viable Product (MVP), OpenAI, Data Lakes, Dashboards, Data Visualization, Flask, Finance, Finance APIs, ChatGPT, Full-stack, Large Language Models (LLMs), Prompt Engineering, Vector Databases, Pinecone, LangChain, LangGraph
Capital Market Expert
2023 - 2024
Visual Candy Systems Limited
- Provided consulting on optimizing algo-trading FX modeling for the client.
- Investigated current performance drivers and optimized feature selection.
- Performed back-testing of historical trades to determine the impact on profit and loss.
Technologies: Scikit-learn, Python, Capital Markets, Machine Learning, Model Development, Model Evaluation, Random Forests, XGBoost
AI Developer
2023 - 2023
Samer Abualsoud
- Developed a trading algorithm based on FX time series using ML/AI techniques.
- Performed back-testing on historical trading data.
- Developed the infrastructure to upload the model online for live trading.
Technologies: Artificial Intelligence (AI), Natural Language Processing (NLP), Machine Learning, Python, Trading, Model Development, Feature Engineering, Model Evaluation, Random Forests, XGBoost, Linear Regression, Deep Learning, Google Cloud Platform (GCP)
Data Science Consultant
2023 - 2023
Acuity Trading LTD
- Consulted with the internal team on alternative data applications in financial modeling.
- Prepared training presentations covering various aspects of alternative data usage, including price prediction and portfolio construction.
- Analyzed internal data to create case studies on possible applications in financial modeling.
Technologies: Data Science, Systematic Trading, Quantitative Research, Algorithmic Trading, Large Language Models (LLMs)
Lead Quant Developer
2022 - 2022
SPS Trading
- Developed the dev library for algorithmic crypto trading.
- Designed and developed trading API for quants to interact with the trading systems.
- Implemented and onboarded algorithmic trading models into production.
Technologies: Python 3, Python, API Design, API Development, C++, C++14, AWS Command Line Interface (CLI), AWS DevOps
Machine Learning Engineer
2022 - 2022
PepsiCo Global - Main
- Developed the Bayesian model for ad allocation using a very long list of features from ad campaigns.
- Trained and maintained the Bayesian model for ad allocation on AWS.
- Performed analysis and presentation of the model's outcomes, specifically feature importance, p-values, and more.
Technologies: Python, Machine Learning, SQL, Amazon Web Services (AWS), Docker, Bayesian Inference & Modeling, Bayesian Statistics, Jupyter Notebook, Visual Studio, Statistical Data Analysis, TensorFlow, Artificial Intelligence (AI), Data Modeling, Data Scientist, Data Science, Data Analysis, Dashboards
Software and Data Engineer
2022 - 2022
Reddit, Inc.
- Developed algorithms to optimize advertisement revenue.
- Implemented model changes in Scala to include new features.
- Researched increasing revenue through better budget pacing techniques.
Technologies: Data Science, Distributed Systems, Software Engineering, Go, Scala, Python, Java, Spark, BigQuery, ETL, Mathematics, Quantitative Analysis, Numerical Analysis, Algorithms, Back-end Development, Machine Learning, Google BigQuery, Machine Learning Operations (MLOps), Jupyter Notebook, Statistical Data Analysis, Data Scientist, Data Analysis, Dashboards, Amazon Web Services (AWS)
Data Scientist
2021 - 2021
Grata Inc
- Created an MVP for estimating a company's value from public data.
- Participated in data gathering and building data pipelines.
- Delivered a stand-alone Python app to run a machine-learning model for live valuations.
Technologies: Data Science, Python, Economics, Natural Language Processing (NLP), Generative Pre-trained Transformers (GPT), PyTorch, Machine Learning, Statistics, Perl, R, Data Pipelines, Jupyter Notebook, Statistical Modeling, Statistical Data Analysis, Data Analytics, Data Modeling, Data Scientist, Optical Character Recognition (OCR), Data Scraping, Feature Engineering, Model Development, Model Evaluation, Random Forests, Linear Regression, Logistic Regression
Python Quantitative Researcher
2021 - 2021
Tickup (Algo Fund)
- Designed and developed an algorithmic trading platform that connected multiple development components and technologies.
- Onboarded trading strategies from quant workspaces into the trading platform. Performed backtesting and parameter optimization under different scenarios.
- Cleaned, managed, and consolidated data prepared for the go-live version.
Technologies: Python, Go, SQL, Machine Learning, Data Engineering, Data Science, Quantitative Analysis, Quantitative Modeling, Quantitative Research, Pandas, Python 3, Quantitative Finance, Finance, Machine Learning Operations (MLOps), Deep Learning, R, Data Pipelines, Financial Forecasting, Artificial Intelligence (AI), Jupyter Notebook, Visual Studio, Statistical Modeling, Statistical Data Analysis, Software Engineering, TensorFlow, Data Analytics, Data Modeling, Data Scientist, BigQuery, Data Analysis
Credit Risk Quant
2018 - 2020
Bank of America Merrill Lynch
- Delivered an IRC/CRM regulatory project dictated by Brexit migration requirements.
- Enhanced aspects of the model to better reflect theoretical requirements and historical behavior. Conducted statistical tests and submitted them to the validation department.
- Improved the performance of the model implementation. Identified the current model's properties, which reduced the execution from days to hours.
Technologies: SQL, Python, C++, Data Science, Data Engineering, Quantitative Analysis, Quantitative Modeling, Pandas, Python 3, Quantitative Finance, Finance, C#, Data Pipelines, Financial Forecasting, Jupyter Notebook, Visual Studio, Statistical Modeling, Statistical Data Analysis, Artificial Intelligence (AI), Data Analytics, Data Modeling, Model Development, Model Evaluation, Monte Carlo Simulations, Simulations
Flow Rates Quant
2018 - 2018
BNP Paribas
- Contributed to the pricing and risk platform of an electronic transformation project.
- Implemented prices and risk-across-rates products such as swaps, bonds, and futures.
- Enhanced the C++ library for pricing and risk calculations.
Technologies: Python, C++, Quantitative Analysis, Quantitative Research, Quantitative Modeling, Finance, Quantitative Finance, C#, Data Pipelines, Financial Forecasting, Jupyter Notebook, Visual Studio, Statistical Modeling, Statistical Data Analysis, Data Analytics, Data Modeling
Quant Developer
2017 - 2018
Bank of America Merrill Lynch
- Collaborated with the model performance team to backtest the bank models for all asset classes.
- Improved and enhanced the Python codebase and user interface.
- Used Python and C++ coding to simulate risk factors and correlations, applied for calculating profit and loss, XVA, and margins.
Technologies: C++, Python, Simulations, Data Engineering, Pandas, Python 3, Finance, Quantitative Finance, Quantitative Analysis, C#, Visual Studio, Statistical Modeling, Statistical Data Analysis, Data Analytics, Data Modeling
Behavioral Modeler
2016 - 2016
Royal Bank of Scotland
- Led the behavioral modeling team in preparation for separating the Williams & Glyn division of the Royal Bank of Scotland.
- Developed predictive behavioral models for residential mortgages and current or savings accounts. The models' owner was the treasury, using them for the purposes of funds transfer pricing (FTP) and interest rate risk management.
- Coordinated the development of the Python library for the team and developed a web-based GUI for business users to run the models.
Technologies: Python, Machine Learning, Scikit-learn, Data Science, Data Engineering, Quantitative Analysis, Quantitative Modeling, Quantitative Research, Finance, Quantitative Finance, Statistical Modeling, Statistical Data Analysis, Data Analytics, Data Modeling
Python Quant Modeler
2014 - 2016
Barclays Bank, PLC
- Developed predictive behavioral models for various portfolios of the bank's investment, corporate, and retail parts using historical time series data; was personally responsible for the residential mortgage book and the corporate term loans book.
- Managed the full lifecycle of the models, from data cleaning to presentation and documentation of results. Performed ad-hoc statistical analyses, scenario analyses, backtesting, and model reviews.
- Contributed to the quant analytics grad training, gaining exposure to all bank departments.
- Performed ad-hoc statistical modeling and statistical data analysis for various projects of the team.
Technologies: SQL, C++, Python, Machine Learning, Scikit-learn, Artificial Intelligence (AI), Quantitative Analysis, Quantitative Modeling, Quantitative Research, Finance, Quantitative Finance, Financial Forecasting, Statistical Modeling, Statistical Data Analysis, Data Modeling
Consultant
2013 - 2014
d-fine, Ltd.
- Conducted current accounts modeling for a major bank based in Vienna.
- Developed a supervisory mechanism for the EU bank regulator.
- Gained exposure and experience in the large-scale application architecture.
Technologies: SQL, Java, C++, R, Python
Experience
House Price Prediction
I developed a Jupyter notebook-based application for the UK house price predictions and trends. There is an optional data scrapping module to refresh most up to date prices.
The client can input their property characteristics such as postcode, number of bedrooms, or garden, and get the estimated price. There is also an add-on feature for trend predictions based on property characteristics.
The client can input their property characteristics such as postcode, number of bedrooms, or garden, and get the estimated price. There is also an add-on feature for trend predictions based on property characteristics.
Sports Arbitrage App
A Python application to detect real-time arbitrage opportunities in the sports market and place appropriate bets. The application constantly reads the odds from a big list of betting websites and identifies the optimal positioning of bets. There is a possibility to place bets for the websites that allow API connections.
Education
2009 - 2013
Doctoral Degree in Theoretical Physics
University of Sussex - Sussex, UK
2007 - 2008
Master's Degree in Theoretical Physics
Imperial College London - London, UK
2001 - 2006
Bachelor's Degree in Physics
Aristotle University of Thessaloniki - Thessaloniki, Greece
Skills
Libraries/APIs
TensorFlow, Scikit-learn, Pandas, NumPy, PyTorch, API Development, OpenAI Assistants API, XGBoost
Tools
PyCharm, Visual Studio, BigQuery, AWS Command Line Interface (CLI), ChatGPT, Algorithm Design, OpenAI Gym, Amazon Simple Queue Service (SQS), Amazon Simple Notification Service (SNS), Claude, Claude Code, Auth0
Languages
Python, C++, R, SQL, Java, Go, Python 3, Scala, C#, Perl, C++14, TypeScript
Paradigms
Quantitative Research, ETL, Testing, Agent-oriented Software Engineering (AOSE), Model Context Protocol (MCP)
Platforms
RStudio, Jupyter Notebook, Amazon Web Services (AWS), Docker, Google Cloud Platform (GCP), Vertex AI, AWS Lambda, CrewAI, Databricks, Langfuse
Frameworks
Spark, Flask, .NET, LangGraph
Storage
Data Pipelines, Data Lakes, Databases, Redshift, Amazon DynamoDB
Industry Expertise
Trading Systems, High-frequency Trading (HFT)
Other
Mathematics, Mathematical Modeling, Statistical Modeling, Statistical Data Analysis, Data Analytics, Statistics, Data Science, Data Modeling, Machine Learning, Artificial Intelligence (AI), Quantitative Analysis, Time Series Analysis, Stan, Correlational Analysis, Technical Leadership, Cloud, Simulations, Predictive Modeling, Web Scraping, Bayesian Inference & Modeling, APIs, Data Engineering, Quantitative Modeling, Quantitative Finance, Finance, Distributed Systems, Software Engineering, Numerical Analysis, Algorithms, Back-end Development, Google BigQuery, Machine Learning Operations (MLOps), Deep Learning, Bayesian Statistics, Economics, Natural Language Processing (NLP), Financial Forecasting, Generative Pre-trained Transformers (GPT), Data Scientist, Optical Character Recognition (OCR), Writing & Editing, Systematic Trading, Algorithmic Trading, API Design, AWS DevOps, Data Scraping, Fine-tuning, Models, Data Analysis, Dashboards, Capital Markets, Large Language Models (LLMs), Minimum Viable Product (MVP), OpenAI, Data Visualization, Finance APIs, Full-stack, Trading, PDF, Data Extraction, Stock Trading, Financial Software, Principal Component Analysis (PCA), Reinforcement Learning, AI Design, Learning to Rank (LTR), ML Pipelines, Large Language Model Operations (LLMOps), Prompt Engineering, LangChain, Vector Databases, Pinecone, AI Agents, Image Recognition, Document Parsing, Amazon Bedrock AgentCore, Retrieval-augmented Generation (RAG), Agentic AI, Open-source LLMs, Agentic RAG Systems, FastAPI, RAG Architecture, Supabase, Feature Engineering, Random Forests, Linear Regression, Model Development, Model Evaluation, Logistic Regression, Monte Carlo Simulations, Multiagent Generative Systems (MAGs), Multi-agent Systems, Multi-agent Orchestration, Conversational AI, LLM Agents
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