Description :

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 to 7 years 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)

- Data engineering (ETL, Spark/Flink)

Nice to Have :

- Distributed training, RLHF, Inferentia/Trainium

- Domain experience (asset/sustainability)

- ML security & compliance

How You Work :

- Outcome-driven and fast-iterating

- Strong engineering and production mindset

- Collaborative leader and mentor