Professional Overview
Senior AI Engineer with 5+ years shipping production LLMs, agentic systems, and RAG pipelines across Germany and India. Led AI R&D at Lenskart, built LangGraph agents at Dialog Matrix, and developed CV perception systems at Bertrandt. M.Sc. TU Darmstadt. Stack: Python, PyTorch, LangChain, LangGraph, AWS, Docker, MLflow.
Experience
AI Expertise
Skills
Employment Type
Description of Responsibilities
- Replaced API-based intent inference with a fine-tuned, quantized SLM
- Designed end-to-end system architecture for the SLM pipeline on AWS EKS
- Led AI R&D; for the voice intelligence initiative
- Benchmarked multiple API against a custom fine-tuned SLM on latency and accuracy
AI Expertise
Skills
Employment Type
Description of Responsibilities
- Enhanced XGBoost-based fraud-login detection ML pipelines on production traffic
- Scraped and assessed Kubernetes pod performance metrics for real-time monitoring
- Integrated model performance KPIs into Grafana dashboards for drift detection
Key Achievements / KPIs
- Improved fraud detection model precision and recall on production traffic
- Reduced mean time to detect model drift via real-time Grafana monitoring
AI Expertise
Skills
Employment Type
Description of Responsibilities
- Built agentic customer support system using LangGraph, ChromaDB, and Pydantic.
- Designed dual-corpus RAG pipeline over 1,000 product docs and 3,000 resolved tic
- Fine-tuned Stable Diffusion with LoRA adapters for few-shot visual concept learn
- Built and deployed FastAPI microservices for TTS model serving
Key Achievements / KPIs
- Achieved RAGAS score of 0.85 on production RAG system
- Automated 40% of support tickets without human intervention
Education
Field of Study: Information and Communication
Field of Study: Electronics and Communication Engineering
Areas of Expertise
AI & Machine Learning Engineers
Design, train, and ship production ML models — from data pipelines to model inference at scale. Covers supervised learning, neural networks, LLM integration, and model optimisation.
MLOps & AI Infrastructure
Architect scalable ML infrastructure — CI/CD for ML, model versioning, experiment tracking, monitoring, and cloud deployment on AWS, GCP, or Azure.
Data Science & Analytics
Extract business insights from raw data using statistical modelling, predictive analytics, A/B testing, and interactive dashboards.
NLP & Prompt Engineering
Build natural language processing systems, fine-tune large language models, and craft reliable production prompts for AI-powered products.