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MLOps Engineer with 1+ years in Generative AI & NLP
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Dhriman Deka is an MLOps Engineer with expertise in generative AI and natural language processing. With 1.1 years of experience, he has engineered AI models for compliance testing, developed BERT-based NLP engines, and implemented scalable MLOps pipelines. He also has strong skills in deep reinforcement learning and portfolio optimization, demonstrating a comprehensive background in data science and machine learning.
Indian Institute of Science Education and Research (IISER) Bhopal
Bachelor of Science · Economics
December 1, 2021 – July 1, 2025
Probe42
Data Science & ML Engineer
May 1, 2025 – Present
Bengaluru, Karnataka, India
LaRGo - Large Language Models
Research Project
February 1, 2024 – August 1, 2024
Kolkata, West Bengal, India
Pension Portfolio Optimiser
June 10, 2026 – Present
Improved retirement portfolio performance by optimizing Max Sharpe, Min Volatility, and Risk Parity strategies on 1,713 daily data points, generating 2.16%-5.04% volatility ranges and up to 12% higher returns than benchmarks. Enhanced risk management by integrating VaR (95% CI), Expected Shortfall, and 10,000+ Monte Carlo simulations; hosted on AWS for real-time user analysis. Accelerated academic research by transforming SSRN paper findings into a modular Python library, enabling 50% faster reproductions for researchers.
View ProjectLYRA - AI-Powered Mental Health Therapy Chatbot
June 10, 2026 – Present
Built a full-stack mental health chatbot by integrating Gemini API and Clerk auth, managing 500+ test conversations with 99% uptime. Improved risk classification by fine-tuning a sentiment model on 8,000+ dialogues, attaining 87% accuracy and lowering false negatives by 25%. Delivered scalable production deployment via end-to-end MLOps — containerized on AWS with Docker and MLflow tracking, supporting 1,000+ users/month.
Comparative Analysis of NPS and UPS from an Employee Perspective and Evaluation of Fiscal Implications of Pension Reforms
SSRN
June 1, 2026 – Present
Teaching AI to Play Ludo: A 10,000-Episode Deep Reinforcement Learning Journey
Medium
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's diverse project portfolio, ranging from academic research (Pension Portfolio Optimiser, LaRGo) to personal projects (LYRA Chatbot) and professional experience (Probe42), indicates a strong drive for continuous learning and application of skills. The involvement in research and publications suggests an inquisitive and collaborative nature. The focus on MLOps and scalable solutions aligns well with a fast-paced, production-oriented environment. The breadth of technologies and problem domains tackled shows adaptability and a willingness to engage with various challenges.
Soft Skills & Operational Fit
The candidate demonstrates strong problem-solving skills by tackling complex issues like manual compliance testing and inaccurate search. Their project descriptions highlight a results-oriented approach, focusing on quantifiable improvements (e.g., 80% validation time reduction, 45% query accuracy lift). The experience with end-to-end MLOps and scaling solutions suggests an operational mindset and an understanding of production requirements. The academic background in Economics, combined with technical skills, indicates an analytical and structured thinking approach.