AI Ml Engineer

Responsibilities

• ● Role & responsibilities 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)