Machine learning engineer (llm+rag+ai modeling)

What You'll Do

Build LLM apps (RAG, prompts, agents, guardrails, evaluation) Own ML lifecycle: data → training (Lo RA/QLo RA) → deployment → monitoring Deploy scalable solutions on AWS (EKS/ECS/Lambda, Sage Maker, Bedrock) Optimize training/inference (FSDP, Deep Speed, 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 (Sage Maker, Bedrock, Lambda, etc.) Python, Py Torch, 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...