AI Engineer / Developer

AI Engineer + Data Scientist

JD

AI Engineer / Developer
Snapshot
Experience: 5-7 years in ML/AI engineering
Reports To: AI Technical Lead / Manager, AIML
Education: B.E./B.Tech or M.Tech in CS, Data Science, or related field
About the Role
Build and ship production-grade GenAI and agentic AI applications that automate enterprise workflows - from design through deployment. A hands-on individual-contributor role for a strong builder.
Key Responsibilities

• Build agentic applications using LangGraph, AutoGen, CrewAI, or Semantic Kernel.
• Design RAG pipelines - chunking, hybrid search, re-ranking, memory, and tool orchestration.
• Deploy and monitor AI workloads on Azure (AKS/ARO) with CI/CD and observability.
• Implement responsible-AI guardrails - prompt-injection defense and content filtering.
• Define evaluation metrics for task success, hallucination, latency, and cost.
  Must-Have Skills
• 5+ years ML/AI engineering with production LLM/agentic delivery.
• Advanced Python and at least one agent framework (LangGraph, AutoGen, CrewAI, PydanticAI).
• Strong LLM and prompt-engineering skills (GPT, Claude, LLaMA); hands-on RAG workflows.
• Azure AI stack (Azure OpenAI, AI Search, AI Services) and Databricks ML (MLflow, Delta Lake).
• Vector databases (FAISS, Pinecone, Chroma) and embedding/retrieval design.
• Containerized deployment (Docker/Kubernetes, AKS/ARO) and REST APIs / WebSockets / event-driven services.
• CI/CD and version control (Jenkins / GitHub Actions, Git) with SDLC and agile practices.
• Portfolio of 3+ production AI deployments with measurable business impact.
  Nice to Have
• Model fine-tuning (LoRA/PEFT), multi-modal AI, and model evaluation frameworks.
• LLMOps / MLOps and model monitoring (drift, latency, cost, hallucination).
• Knowledge graphs (Neo4j); AWS Bedrock / GCP Vertex AI exposure.
• AI-augmented dev tools (GitHub Copilot, Claude Code, Windsurf) for rapid prototyping.
• Enterprise AI security, compliance, and governance awareness.
• Manufacturing or supply-chain domain experience.

AI Engineer + Data Scientist - Lead

JD

AI Technical Lead
Snapshot
Experience: 8+ years in ML/AI (incl. DL/RL in production)
Reports To: Manager, AIML
Education: Masters (preferred) or Bachelors in CS, Data Science, or Mathematics
About the Role
Own the full lifecycle of enterprise-scale AI solutions - architecture through production - and set technical best practices, governance, and standards across the team. A player-coach leadership role.
Key Responsibilities

• Architect end-to-end DL/RL and agentic AI solutions from design to production.
• Set technical standards, governance, and evaluation frameworks across the team.
• Optimize models for production inference (TensorRT/ONNX/Triton); balance accuracy vs. latency.
• Scale training/inference on Azure ML, AKS/ARO, and distributed infrastructure.
• Lead technical solutioning, manage stakeholders, and mentor engineers.
  Must-Have Skills
• 7+ years ML/AI with production DL and/or RL systems.
• Mastery of DL frameworks (PyTorch/TensorFlow/JAX) and strong applied math (linear algebra, probability, optimization).
• Deep learning across CNNs, transformers, and sequence models; RL agents (PPO, SAC, TD3, CQL).
• Inference optimization (TensorRT/ONNX/Triton) and accuracy vs. latency benchmarking.
• Model serving and containerized deployment at scale (Docker/Kubernetes, AKS/ARO).
• Cloud-scale training/inference on Azure ML with distributed training and MLOps/CI-CD.
• Ability to define standards, governance, and evaluation frameworks across a team.
• Proven technical leadership, mentoring, and stakeholder communication.
• Track record of 6-10 production deployments with measurable business impact.
  Nice to Have
• Agentic AI architecture (LangGraph, AutoGen, CrewAI) and RAG pipeline design.
• RL libraries (Ray RLlib, Stable-Baselines3, Gymnasium); multi-agent RL and simulation (MuJoCo).
• Model compression/quantization and GPU-efficiency optimization.
• Agentic platform evaluation (Azure AI Foundry, AWS Bedrock, Databricks AgentBricks).
• Optimization/OR background (LP/MIP, Gurobi/CPLEX); Julia/SciML exposure.
• Semiconductor, manufacturing, or supply-chain domain experience.

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