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Summary
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Machine Learning and GenAI engineer, expert in LLM-as-Judge evaluation, Agentic AI systems, and production ML across on-premises and Databricks. Deliver end-to-end MLOps from ingestion to inference and integrates LLMs into backend architectures
- Advanced in Agentic AI, GenAI-driven design and implementation across domains, on-premises and Databricks
- Expert in MLOps lifecycle with focus on NLP/numeric ingestion, preprocessing, feature engineering/selection, model design, training, validation, inference
- Expert in GenAI evaluation
- Expert in statistical A/B testing
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Qualifications
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Industry Expertise
- Life Sciences, Search Solutions, Media & Entertainment, Telecommunications, High Technology, Software & Services, FinTech, Financial Services, Analysis of commercial banks reports, Counterparty Default Risk Management, Margin trading
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Technical Skills
Applied & Formal Sciences
- Artificial intelligence, Machine Learning, Probability Theory, Statistical Analysis and Modeling, Regression Analysis, TRIZ, Graph Theory, Combinatorics, Algorithms & Data Structures, Software Development
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Data & Analytics Engineering Practices & Technologies
- GenAI Evaluation Metrics, LLM Evaluation, Ranking Metrics, Clustering Metrics, Recommender System Metrics, Classification Metrics, Regression Metrics, Offline Evaluation, Online Evaluation, Model Agnostic Interpretation, NLP Preprocessing, NLP Labelling, Text to Speech, Speech to Text, Sequence-to-Sequence, Factorization-based Machine Learning Models, Collaborative Filtering Recommender Systems, Content-based Recommender Systems, Semantic Textual Similarity, Searching Algorithms, Information Retrieval, Tabular Data, Tree-based Machine Learning Models, Machine Learning Model Development, Ensembling, Parameter Optimization, Hyperparameter Optimization, Feature Engineering, Synthetic Data Generation, Interactive Dashboards, Databricks for Data Analytics, Python for Data Analytics, A/B Testing
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Software Engineering Practices & Technologies
- Gen AI Application Development, AI Agents Design and Integration, AI Agents Development, Gen AI Assisted Development, Prompt Engineering, Python Core, Python Asynchronous Programming, Python ML, Core Java Development, Java Troubleshooting, Java Microservice Infrastructure, Software Design, Cloud Fundamentals, CI/CD, Branching Strategies, Gitflow, Software Engineering Practices, Writing API Documentation
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Frameworks, Libraries & Platforms
- Torch, Transformers, LangChain, Spark, Databricks, LightGBM, XGBoost, CatBoost, Scikit-Learn, Scipy, Pandas, Plotly, AsyncIO, Python, Docker, Spring, Java, PostgreSQL
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Quality Engineering
- Performance Optimization, Performance Testing
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