Gen AI

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