Job Overview:

We are looking for a Senior GenAI Developer to design, build, and productionize agentic AI systems—LLM-powered agents that can plan, use tools, orchestrate workflows, and operate reliably under enterprise constraints. You will own key parts of the agent architecture (planning, tool use, memory, evaluation, safety/guardrails, and observability) and deliver end-to-end solutions across RAG, function/tool calling, multi-agent coordination, and scalable deployment.

Key Responsibilities

• Design and implement agentic systems: single-agent and multi-agent architectures (planner/executor, supervisor-worker, routing, reflection, critique, task decomposition).
• Build robust tool-using agents: function calling, tool schemas, tool authorization, retries, rate limiting, and sandboxing.
• Implement RAG + memory patterns: retrieval strategies, hybrid search, context assembly, long-term memory, and grounding/citation behaviors.
• Develop workflow orchestration for agent execution (state machines/graphs), concurrency controls, and deterministic execution where possible.
• Productionize GenAI services: APIs, background jobs, streaming responses, caching, and cost/latency optimization.
• Establish agent evaluation: golden sets, simulation-based evals, LLM-as-judge with mitigations, task success metrics, regression testing.
• Build observability and safety: tracing, token/tool telemetry, anomaly detection, prompt injection defenses, data leakage prevention, policy enforcement.
• Collaborate with product, security, and platform teams to deliver enterprise-ready solutions and integrate with internal systems (data, identity, workflow).
• Mentor engineers, set coding standards, and contribute to architecture reviews and technical roadmaps.

Required Qualifications

• 6+ years software engineering experience; 2+ years building LLM/GenAI systems in production.
• Strong programming skills in Python (required) and/or TypeScript/Node.js.
• Hands-on experience building agents (tool calling, planning, routing, multi-step workflows) beyond simple chatbots.
• Solid understanding of prompting, context window management, grounding, hallucination failure modes, and mitigation strategies.
• Experience with RAG: embeddings, vector databases, chunking strategies, hybrid retrieval, re-ranking.
• Proven ability to ship production services: Docker/Kubernetes, REST/gRPC, CI/CD, monitoring, incident response basics.
• Strong data/security instincts: secrets management, PII handling, secure tool access, least privilege