Senior GenAI / LLM Engineer (RAG, MLOps)

Senior GenAI / LLM Engineer (RAG, MLOps)

Must‑Have Skills

• Strong foundations in GenAI, ML modeling, Data Science, Statistics, and AI fundamentals (NLP, Neural Networks, LLMs)
  
• Hands‑on with major LLMs: Google Gemini, OpenAI, Anthropic Claude, Mistral, Llama + open‑source models
  
• Critical: Deep, hands‑on experience building Retrieval‑Augmented Generation (RAG) pipelines (advanced RAG techniques + implementation)
  
• Strong Prompt Engineering (prompt strategies, tuning, reusable templates) and agentic frameworks
  
• Python (mandatory) with strong experience across: Pandas, NumPy, scikit‑learn, PyTorch/TensorFlow, Transformers, FastAPI, LangChain, LlamaIndex
  

Data / Integration

• Experience integrating GenAI into enterprise apps using APIs, orchestration tools, and knowledge/graph concepts
  
• Vector DB experience: PGVector, Pinecone, MongoDB Atlas, Neo4j (or similar)
  
• Experience handling large‑scale unstructured data and high‑throughput processing
  

Deployment / MLOps

• Critical: Proven experience deploying GenAI/LLM solutions to production
  
• Strong MLOps knowledge: evaluation, monitoring, and robust deployment pipelines
  
• CI/CD tools exposure: Jenkins, GitLab CI, Azure DevOps, ArgoCD
  
• Container orchestration: Kubernetes or OpenShift
  

Other / Soft Skills

• Strong problem solving, stakeholder collaboration, and ability to work independently on ambiguous problems
  
• Working knowledge of Guardrails and methods to assess performance/safety of GenAI features