Senior AI & Agentic System Engineer

We’re building agentic AI systems that autonomously reason, plan, and act across complex workflows. You’ll be a core contributor designing and shipping production-grade AI agents, integrating with LLM providers, orchestrating multi-step workflows, and building the infrastructure that makes agents reliable, observable, and scalable. This is a deeply technical role — you’ll work at the intersection of LLM engineering, backend systems, and agent architecture.

Job Title - Senior AI & Agentic System Engineer

Location - Gurgaon, India (You may note that this role is fully remote)

Level – Senior (5+)

Team – AI Platform / Engineering

WHAT YOU’LL DO

• Design and build AI agents — autonomous systems that use tools, browse the web, call APIs, manage memory, and complete multi-step tasks with minimal human intervention

• Architect agentic workflows — orchestrate multi-agent pipelines (planner → executor → verifier patterns), handle tool-use loops, HITL checkpoints, retries, and failure recovery

• Integrate LLM providers — work with Anthropic Claude, OpenAI, Gemini; implement prompt caching, structured outputs, and context management at scale; select the right model for the right task

• Build knowledge pipelines — design RAG systems, hybrid retrieval, vector stores, embedding pipelines, and memory layers that give agents contextual awareness

• Own backend reliability — REST/GraphQL APIs, async job queues, observability, latency optimization, cost tracking for token usage

• Drive system design decisions — lead architecture discussions, define component boundaries, evaluate build vs. buy tradeoffs, and document decisions (ADRs, diagrams)

• Maintain CI/CD pipelines — keep deployments fast, safe, and automated across dev/staging/prod environments

• Evaluate agent behavior — build LLM tracing, regression frameworks, and evals pipelines to detect behavioral drift, intent failures, and quality degradation across model updates

• Stay ahead of the curve — evaluate emerging standards (MCP, A2A, reasoning models, new frameworks) and make adoption recommendations

WHAT WE’RE LOOKING FOR

• Deep experience with at least one major LLM provider SDK (Anthropic, OpenAI, Gemini)

• Prompt engineering depth: few-shot design, chain-of-thought, structured outputs, tool/function calling, multi-turn context management, prompt caching, token budgeting

• Built real production agents — not just chatbots; systems that take actions, use tools, and operate autonomously

• Understands agent patterns: ReAct, plan-and-execute, multi-agent orchestration, reflection loops, supervisor/worker hierarchies

• Familiar with MCP (Model Context Protocol), A2A, and agent frameworks (LangGraph, CrewAI, AutoGen) — and when NOT to use them

• Knows how to handle non-determinism: retry strategies, output validation, graceful degradation

Emerging AI Landscape

• Reasoning models — knows when to use o1/o3, Claude Extended Thinking vs. standard models; treats model selection as an architectural decision, not an afterthought

• Model routing & cascading — routes queries to right-sized models (Haiku/Flash for simple, Opus/GPT-4o for complex) based on cost/capability tradeoffs; doesn’t default to the largest model for everything

• Compound AI systems — combine multiple specialized models and tools in pipelines rather than relying on a single general-purpose model

• Human-in-the-loop (HITL) — designs checkpointing for long-running agents, approval workflows, interrupt/resume patterns; knows when and where agents need human gates

• Persistent agent state — durable execution patterns (LangGraph checkpointers, Temporal-style workflows); agents that survive restarts and partial failures

• Prompt injection & AI security — understands attack surfaces unique to tool-using agents; builds with guardrails in mind (NeMo Guardrails, Llama Guard, output schemas)

• Semantic caching & batch inference — reduces LLM costs through response-level semantic caching and async batch APIs; thinks about cost at 10x before it becomes a problem

LLMOps & Evaluation

• LLM-specific observability: Langfuse, LangSmith, Helicone, or Arize Phoenix — prompt/response tracing, token cost per flow, per-step latency, session replay

• Evals methodology: RAGAS, DeepEval, Promptfoo, or Braintrust — systematic evaluation of LLM outputs, not vibes-based testing

• Treats prompts as code: versioning, A/B …