Gen AI Architect

• Experience: 12-15years
• Location: Noida/Bangalore
• Requirements: Experience in Observability and OpenTelemetry - LLM/Applications
• Send resumes to: [email protected] with below details:
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Job Description:

Level: 12+ years in software or platform engineering, including 3+ years designing

GenAI systems that other engineers shipped.

Own the architecture for LLM applications and the platform under them: models,

data, agents, evaluations, delivery, and the controls around them.

You will

 Set the architecture for model access, retrieval, agent runtime, shared tools,

and one place to see traces and scores.

 Choose when one agent is enough and when work splits across agents. MCP

is the tool boundary. Multi-agent handoff, including A2A, is the agent

boundary.

 Define RAG so index lifecycle, citations, and freshness are part of the design,

and judge whether the enterprise data can support it.

 Set standards others implement: OpenTelemetry GenAI as the span contract,

an evaluation approach so scores stay comparable, and LLMOps so prompts,

agents, and indexes are versioned, regression-tested, and rollback-able.

 Place guardrails, tenancy, secrets, and retention in the platform. Decide what

an agent may read or change, and where a person must approve.

 Shape inference for latency and cost: model routing, smaller models where

they are enough, caching, and batching where they help.

 Write the architecture down and defend the tradeoffs with engineering and

with the people who own the business outcome.

Skills

 A system you can walk through from request to stored trace, score, and cost,

on a cloud you have operated: AWS, Azure, or GCP, including Bedrock, Azure

OpenAI, Vertex AI, or Databricks.

 One agent framework at design depth: LangGraph, CrewAI, AutoGen,

Semantic Kernel, OpenAI Agents SDK, or Google ADK. Familiarity with the

others is enough.

 RAG architecture: hybrid retrieval, reranking, grounding, and the data work

underneath the index.

 MCP and multi-agent design, including memory, tool scope, and human

approval. Direct A2A experience is a plus.

 Evaluation and observability standards: golden sets, live signals, LLM-as-

judge, and GenAI spans for model, retrieval, tool, and agent steps. Operating

experience with MLflow, Arize Phoenix, LangSmith, or Langfuse is the

evidence. You will set the gen_ai.* contract here. You do not need to have

authored that spec elsewhere.

 LLMOps: CI/CD for prompts and agents, regression gates, model and index

rollout, and rollback.

 Platform controls: isolation between tenants and agents, guardrails, audit,

retention, and token cost.

 Safety and reliability: prompt injection, tool abuse, timeouts, and a defined

path when the model is wrong.