Role Description
Primary Skills: Agentic AI, Pytorch, RAG, ML, Gen AI Description: Key Responsibilities
- Build and deploy scalable LLM, RAG, and agent-based systems
- Architect LLM inference and deployment pipelines
- Optimize models for efficient and cost-effective production
- Collaborate with data science, research, and product teams
- Ensure clean code, testing, reproducibility, and CI/CD
- Mentor junior engineers and drive engineering best practices
- Ensure ethical, secure, and responsible AI development Required Skills
- Advanced Python with strong fundamentals in NumPy, Pandas, scikit-learn
- Deep learning expertise in PyTorch / TensorFlow
- Hands-on with LLM frameworks: Hugging Face Transformers, LangChain (prompting & fine-tuning)
- Strong experience with Agentic AI frameworks: AutoGen, CrewAI, LangGraph
- Expertise in RAG pipelines, semantic search, vector databases
- Strong software engineering practices: microservices, TDD, concurrency
- Ability to rapidly prototype and productionize GenAI solutions ________________________________________ Good-to-Have Skills
- Model optimization: Quantization (GPTQ, AWQ), pruning, distillation
- Multimodal AI (text, vision, audio): CLIP, BLIP, Whisper, LLaVA
- LLM serving using FastAPI and vector DBs (FAISS, Pinecone, Chroma)
- CI/CD pipelines, Airflow, Docker, Kubernetes / Helm
- Cloud AI deployments on AWS / Azure / GCP (e.g., SageMaker)
- MLOps & tracking: Git, MLflow
- Data pipelines & ELT/ETL using Snowflake
Skills
Python, PyTorch, Agentic AI, RAG