Description

What you'll do:

Design and ship GenAI solutions using LLMs, RAG architectures & advanced prompt engineering
Build AI evaluation frameworks (DeepEval, Ragas) to keep quality and reliability honest
Architect scalable AI/ML pipelines on Azure following MLOps best practices
Develop AI agents and multi-agent systems for complex autonomous workflows
Implement responsible AI — bias detection, safety guardrails, hallucination mitigation & monitoring
Lead both traditional ML and GenAI model development
Mentor junior engineers and help shape technical strategy


 What you bring:

5+ years hands-on AI/ML development
1–2+ years of production GenAI/LLM experience 

Strong Python + deep learning frameworks (PyTorch, TensorFlow, or JAX)
Extensive Azure AI experience — Azure OpenAI Service, Azure ML, Cognitive Services
Hands-on LLM fine-tuning, prompt engineering & RAG implementation
Proven track record building large-scale ML systems in production
Solid DevOps/MLOps and CI/CD fundamentals
Bachelor's or Master's in CS, ML, Data Science or related field


 Nice to have:

LLM frameworks — LangChain, LlamaIndex, Semantic Kernel, AutoGen
Vector databases — Pinecone, Weaviate, ChromaDB, Azure AI Search
Terraform, Docker, Kubernetes / AKS
Model optimization — quantization, distillation, efficient inference
MLflow, Weights & Biases
Deep grasp of transformer architectures & attention mechanisms
Open-source contributions or published research

Key Skills

Education

Any Graduate