What You’ll Do

• Build LLM apps (RAG, prompts, agents, guardrails, evaluation)
• Own ML lifecycle: data → training (LoRA/QLoRA) → deployment → monitoring
• Deploy scalable solutions on AWS (EKS/ECS/Lambda, SageMaker, Bedrock)
• Optimize training/inference (FSDP, DeepSpeed, vector DBs, GPUs)
• Set up MLOps: CI/CD, model tracking, governance, responsible AI
• Collaborate with product/UX to deliver impactful ML solutions
• Mentor team and lead technical decisions
• Perform EDA and domain-focused data research

What You’ll Bring

Required:

• 5–8 and 7-12 yrs experience (3+ yrs in production ML systems)
• 1+ yr hands-on with LLMs (RAG, fine-tuning, prompt engineering)
• Strong AWS experience (SageMaker, Bedrock, Lambda, etc.)
• Python, PyTorch, Hugging Face, vector DBs
• MLOps (CI/CD, Docker, Kubernetes, monitoring)