AI Engineer

• Work directly with clients: translate business problems into AI solutions, explain how the technology behaves (and its limits), and report progress clearly;
• Support proposals and pre-sales for new AI engagements;
• Lead AI engineers on engagements: if needed to have ability to set tasks, review work, give feedback, and support growth and performance;
• Own delivery quality and the path from PoC to production;
• Design end-to-end AI-powered experiences and the systems behind them: model selection, deployment patterns, infrastructure;
• Architect and build RAG pipelines - chunking (semantic/recursive/hierarchical), embedding selection, retrieval, hybrid search, re-ranking, and evaluation;
• Build agentic/multi-step workflows (LangChain, LangGraph, or equivalent): multi-agent designs, agent-to-agent protocols, and tool/API/MCP-style integration with enterprise systems;
• Deploy on a hyperscaler AI platform (Azure AI Foundry, AWS Bedrock, Google Vertex AI, K8s based or equivalent);
• Build for production: observability, evaluations, AI safety, bias/failure-mode handling, security, and cost control;
• Write the production code yourself - hands-on;
• Use AI development tools (GitHub Copilot, Cursor, Claude Code, etc.) as part of how you build and validate what they produce.

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Senior AI Engineer