AI Engineer
Posted 25 Jun 2026
Advertised as “Senior AI Engineer”
BranchFactor Summary
The original posting with the fluff stripped out
- Management level
- People management
- Employment
- Hybrid
- Contract
- Full-time
No salary listed for this role.
About the roleThere’s a wide gap between an agent that works in a demo and one that works across millions of live transactions. Closing it is the job. You’ll embed with the teams and customers who depend on AI — risk, fraud, collections, payments, support, developer experience — and design, build, and ship agentic systems into their production environments. You’ll also help build the platform underneath: Paytm’s AI inference platform (Pi) and the agentic runtime, orchestration, and tooling that lets agents reason, plan, use tools, and run multi-step workflows safely.
What you’ll do
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Embed & deploy
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Tackle greenfield problems alongside internal teams and customers — scope ambiguous needs and build agents from scratch that fit how they actually work.
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Own deployments end-to-end: discovery, build, integration, activation, and the tuning that earns trust and adoption.
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Lead pilots and demos, drive adoption, and clear blockers before they stall a rollout.
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Build agentic systems
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Architect agentic systems — reasoning, planning, tool use, memory, multi-agent coordination — that run real workflows with guardrails.
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Build safe tool-use infrastructure across APIs, databases, and services, with permissioning, sandboxing, and human-in-the-loop.
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Ship SDKs, patterns, and reusable blueprints so internal teams build and deploy agents fast.
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Make it reliable
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Design and run rigorous evals: measure quality, catch regressions, and feed results back into the system.
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Build observability, tracing, and guardrails that prove agents are safe and keep them safe as models and data drift.
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Own the multi-model inference your agents depend on (text, voice, code, vision) — latency, throughput, and cost.
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Lead
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Set technical direction and standards for agentic systems; mentor engineers and partner with ML, product, and security.
What you’ll bring
- 5+ years in software engineering, with 3+ in AI systems or LLM applications, and production systems shipped end-to-end.
- Strong grasp of LLM agent architectures (ReAct, RAG, tool use, multi-agent) and hands-on agentic orchestration and evaluation.
- Proficiency in Python across a broad stack — pipeline, agent, service, and instrumentation.