Lead AI/ML Engineer

• 4-7 years of hands-on experience in ML Engineering, AI Engineering, or LLM Engineering roles
• MANDATORY: At least 2 production-deployed AI/ML systems (describe in application: problem, scale, your ownership, tech stack used)
• Proven experience deploying LLM applications - RAG pipelines, AI chatbots, autonomous agents, or AI-powered workflows - to production cloud (AWS / Azure / GCP)
• Hands-on experience with at least one agentic AI framework: LangChain, LlamaIndex, AutoGen, CrewAI, or Semantic Kernel
• Strong Python proficiency; experience with FastAPI or Flask for building AI-serving microservices
• Experience integrating at least one LLM API in production: OpenAI, Anthropic, Azure OpenAI, Cohere, or Hugging Face
• Hands-on experience with vector databases (Pinecone / Weaviate / Milvus / Chroma / pgvector / FAISS) in production RAG systems
• Solid command of Docker and Kubernetes for containerized AI model deployment
• Understanding of prompt engineering, context window management, and LLM cost optimization
• Exposure to MLOps tooling: MLflow, Weights & Biases, or DVC for experiment tracking and model management

Good to Have

• Experience with multi-agent frameworks: AutoGen, CrewAI, LangGraph, or custom orchestrators
• LLM fine-tuning using LoRA, QLoRA, PEFT, SFT, RLHF, or DPO on domain-specific datasets
• Inference optimization experience: vLLM, TensorRT-LLM, ONNX, model quantization (INT4/INT8), or distillation
• GenAI observability experience with LangSmith, LangFuse, Arize AI, or Evidently AI
• Knowledge of responsible AI, AI governance, output guardrails, bias detection, and factuality validation
• Open-source contributions to GenAI, LLM, or agentic AI tooling
• Frontend exposure (React, TypeScript) for building internal AI dashboards or chat interfaces
• Familiarity with enterprise AI compliance, data privacy, and security best practices (SOC 2, GDPR)