AI Engineer

Lead the design, development, deployment, and optimization of enterprise-grade Generative AI solutions. Build RAG, agentic, multi-agent, and multimodal systems; integrate LLMs with enterprise APIs and data; manage vector search and retrieval pipelines; and support governance, security, monitoring, scalability, and cost optimization.

Must-have skills

• Python programming
• API development using FastAPI or similar frameworks
• OpenAI
• Azure OpenAI
• Anthropic Claude
• Google Gemini
• LangChain
• LangGraph
• OpenAI Agents SDK
• Semantic Kernel
• Generative AI application development
• Agentic AI and multi-agent workflows
• Tool orchestration and memory management
• Human-in-the-loop capabilities
• Retrieval-Augmented Generation (RAG)
• Embeddings and chunking
• Vector search and reranking
• Enterprise knowledge retrieval
• Model Context Protocol (MCP)
• Function calling
• Enterprise tool integrations
• Vector databases: Pinecone, ChromaDB, pgvector, FAISS, or Azure AI Search
• Prompt engineering and context engineering
• Prompt optimization and AI evaluation
• PostgreSQL
• MongoDB
• Redis
• Enterprise data integration
• Analytical and problem-solving skills
• Debugging
• Communication and stakeholder management

Good-to-have skills

• AWS
• Docker
• OpenShift
• Kubernetes
• CI/CD pipelines
• AI monitoring and observability
• Guardrails and Responsible AI practices
• Secure and scalable AI deployments
• Collaboration with product, architecture, and engineering teams