Senior Machine Learning Engineer — Generative AI, Reinforcement Learning, and MLOps
Open to collaborating on impactful AI systems
Senior ML Engineer with 8+ years building end-to-end AI systems—from research to reliable, production-grade services. Experienced across the full ML lifecycle: generative modeling, RL for control, scalable backends, and MLOps that shorten time-to-value. Deep expertise in advanced RAG (graph RAG, hierarchical RAG, multi-hop retrieval) for production LLM systems.
- Medical Imaging Anomaly Detection: 92% top‑1 accuracy; integrated with hospital APIs for real-time clinical alerts.
- RL Optimal Control for Manufacturing: +1.5 tons/hour clinker output; ~$500K verified annual savings.
- MLOps for Generative AI: Reduced model deployment time by 40% for Transformers/Diffusion/LLM agents.
- AI/ML: Generative AI (Transformers, GANs, Diffusion), RL, NLP (LLM fine-tuning; advanced RAG: graph, hierarchical, multi-hop; agents), CV (detection, segmentation), statistical modeling
- Backend/Cloud: Microservices, REST APIs (FastAPI, Flask), SQL/NoSQL, AWS/GCP, serverless
- MLOps/CI/CD: Docker, Kubernetes, MLflow, Weights & Biases, GitHub Actions, Jenkins, Terraform, monitoring (Prometheus, Grafana)
- Languages/Libraries: Python, SQL, R, JavaScript, PyTorch, TensorFlow, Hugging Face, LangChain, scikit-learn, Pandas, OpenCV
- Generative AI for synthetic medical data (diffusion models)
- LLM agents with custom toolchains for multi-step reasoning