Data Science - ML

Key Responsibilities

•      Architect enterprise-grade ML, GenAI, and Agentic solutions.

•      Design the overall AI reference architecture including data pipelines, feature stores, model training, model serving, RAG pipelines, and secure prompt orchestration.

•      Implement LLM fine-tuning/PEFT, vector search, embeddings pipelines, and multi-agent frameworks.

•      Create blueprints for retrieval-augmented generation, domain-specific model training, and guardrails/RLHf pipelines.

•      Work with cloud engineers to deploy GPU-based training and inference environments.

•      Define standards for experiment tracking, versioning, reproducibility, model governance, and observability.

•      Conduct exploratory data analysis, feature engineering, model tuning, and performance benchmarking.

•      Build reusable assets, notebooks, and model templates for rapid use case scale-up.

Required Skills

•      8–12 years experience in ML/Deep Learning/NLP/GenAI.

•      Experience in building Agentic AI solutions.

•      Strong expertise in transformers, LLM fine-tuning, vector search, agent frameworks.

•      Hands-on with RAG based solutions, PyTorch/TensorFlow, LangChain/LlamaIndex, HuggingFace stack.

•      Experience deploying models on AWS using EC2, EKS, custom GPU clusters.

•      Strong understanding of private AI infra (non-managed services), including self-hosted vector DBs, model servers, and orchestration layers.

•      Experience building MLOps pipelines using non-managed components (custom K8s, GitHub Actions, or Jenkins-based CI/CD).

•      Strong experience in Innovation with the ability to create reusable components and frameworks.