About the role
There’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.

Why join?
Be among the first to define how agentic AI ships across a company running payments and credit at massive scale — with direct line of sight from your work to the outcome, and broad ownership across both the platform and the field.

Go Big or Go Home!

Paytm Labs believes in diversity and equal opportunity and we will not tolerate any forms of discrimination or harassment. Our people are critical to our success and we know the more inclusive we are, the better our work will be.
We thank all applicants, however, only those selected for an interview will be contacted.
Paytm Labs is committed to meeting the accessibility needs of all individuals in accordance with the Accessibility for Ontarians with Disabilities Act (AODA) and the Ontario Human Rights Code (OHRC). Should you require accommodations during the recruitment and selection process, please let us know.

What you’ll do
Embed & deployTackle greenfield problems alongside internal teams and customers — scope ambiguous needs and build agents from scratch that fit how they actually work.Own deployments end-to-end: discovery, build, integration, activation, and the tuning that earns trust and adoption.Lead pilots and demos, drive adoption, and clear blockers before they stall a rollout.Build agentic systemsArchitect agentic systems — reasoning, planning, tool use, memory, multi-agent coordination — that run real workflows with guardrails.Build safe tool-use infrastructure across APIs, databases, and services, with permissioning, sandboxing, and human-in-the-loop.Ship SDKs, patterns, and reusable blueprints so internal teams build and deploy agents fast.Make it reliableDesign and run rigorous evals: measure quality, catch regressions, and feed results back into the system.Build observability, tracing, and guardrails that prove agents are safe and keep them safe as models and data drift.Own the multi-model inference your agents depend on (text, voice, code, vision) — latency, throughput, and cost.LeadSet 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.Production experience on AWS and Azure with containerized deployments (Docker, Kubernetes).Strong customer and stakeholder instincts; able to impose structure on ambiguity and push back when needed.A bias toward shipping and comfort operating without a clean spec.Solid understanding of agentic security risks (prompt injection, privilege escalation, data leakage).Strong written and verbal communication.

Nice to have
Agentic systems in regulated industries (fintech, payments, credit, healthcare).Cloud AI/ML services (AWS SageMaker / Bedrock, Azure ML / Azure OpenAI); multi-cloud or hybrid.MCP or agent communication standards; agent evaluation and observability tooling.Model serving (vLLM, TensorRT-LLM, Triton), fine-tuning, quantization, or LoRA.Workflow orchestration (Temporal, Airflow, Prefect) for AI workloads; voice / multimodal / edge inference.Testing and verification for non-deterministic AI systems.

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