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Staff AI Platform Engineer - Inference & Agentic Systems

Paytm · Hybrid

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Last seen by MeritLog September 12, 2026Source: LeverSource version: lever-postings-v1

Source: the employer's Lever job board. Open the original listing for current details. Availability is not verified for this retained page.

Job details

Work model: Hybrid
Salary: Not listed by source
Location: Toronto, Canada

What the role asks for

What you'd do

Inference & Model Serving
Build and operate multi-model serving across modalities (text, voice, code, vision) on shared infrastructure
Own the model lifecycle: download, deploy, serve, monitor, update, swap
Drive inference optimization: latency, throughput, cost - including quantization, batching, caching, and routing strategies
Ensure inference is fast and reliable for the agents and systems that depend on it
Agentic Systems
Architect and build the Agentic AI Platform - runtime infrastructure, orchestration systems, and developer tooling for autonomous agents
Design multi-agent coordination systems enabling agents to collaborate and solve complex workflows
Build robust tool-use infrastructure that allows agents to interact with APIs, databases, and services safely
Implement workflow automation: agents that execute multi-step business and engineering tasks with appropriate guardrails
Build safety and guardrail systems including permissioning, sandboxing, and human-in-the-loop workflows
Develop evaluation and observability frameworks to measure agent behaviour, detect regressions, and debug failures
Develop SDKs and APIs that allow internal teams to build and deploy agents quickly and safely
Platform & Technical Leadership
Define technical direction and architecture for agentic systems across the organization
Build patterns and standards for agent design, tool calling, and evaluation
Partner closely with ML, product, and security teams to deliver production-grade agent systems
Mentor engineers and contribute to best practices for agent system design

What they're asking for

8+ years of software engineering experience, with 3+ years in AI systems or LLM applicationsExperience
Strong understanding of LLM-based agent architectures: tool use, multi-step workflows, multi-agent coordination, and their failure modesSkill
Experience building highly reliable distributed systemsSkill
Experience evaluating LLM systems in production: building evals, detecting regressions, and debugging non-deterministic failuresSkill
Proficiency in TypeScript or Python, and willingness to work in both: the agent platform is TypeScript on Bun with Temporal workflows on Kubernetes and EC2, the inference platform is Python.Skill
Experience working with modern LLM APIs or open-source modelsSkill
Experience with or strong interest in model serving (vLLM, TensorRT-LLM, Triton)Skill
Understanding of distributed systems: task queues, event-driven architectures, state management, and durable long-running workflowsSkill
Experience with cloud platforms (AWS, GCP) and containerized deploymentsSkill
Strong understanding of security risks in agentic systems (prompt injection, privilege escalation, data leakage)Skill
Demonstrated experience leading complex technical initiativesSkill
Strong written and verbal communication skillsSkill

Parsed by MeritLog from the employer’s own posting. The full description follows below.

Job description

About the Role We are a small team of AI builders in Paytm Labs. As a Staff AI Platform Engineer, you will work across inference and agentic systems. You will contribute to Paytm's AI inference platform (Pi), serving internal teams and enterprise customers - running our own coding and domain-specific models (voice, vision, risk, fintech workflows) as well as third-party models. You will also architect and build the platform that enables autonomous AI agents to operate safely and reliably in production - the runtime, orchestration, and developer tooling for agents to reason, plan, use tools, and execute complex multi-step workflows, automating both software development and business processes. You will work at the intersection of LLMs, distributed systems, and production fintech infrastructure, helping define how inference and agentic AI are built and deployed across payments, risk, fraud, collections, support, and developer experience. What You'll Do • Inference & Model Serving • Build and operate multi-model serving across modalities (text, voice, code, vision) on shared infrastructure • Own the model lifecycle: download, deploy, serve, monitor, update, swap • Drive inference optimization: latency, throughput, cost - including quantization, batching, caching, and routing strategies • Ensure inference is fast and reliable for the agents and systems that depend on it • Agentic Systems • Architect and build the Agentic AI Platform - runtime infrastructure, orchestration systems, and developer tooling for autonomous agents • Design multi-agent coordination systems enabling agents to collaborate and solve complex workflows • Build robust tool-use infrastructure that allows agents to interact with APIs, databases, and services safely • Implement workflow automation: agents that execute multi-step business and engineering tasks with appropriate guardrails • Build safety and guardrail systems including permissioning, sandboxing, and human-in-the-loop workflows • Develop evaluation and observability frameworks to measure agent behaviour, detect regressions, and debug failures • Develop SDKs and APIs that allow internal teams to build and deploy agents quickly and safely • Platform & Technical Leadership • Define technical direction and architecture for agentic systems across the organization • Build patterns and standards for agent design, tool calling, and evaluation • Partner closely with ML, product, and security teams to deliver production-grade agent systems • Mentor engineers and contribute to best practices for agent system design What You'll Bring • 8+ years of software engineering experience, with 3+ years in AI systems or LLM applications • Strong understanding of LLM-based agent architectures: tool use, multi-step workflows, multi-agent coordination, and their failure modes • Experience building highly reliable distributed systems • Experience evaluating LLM systems in production: building evals, detecting regressions, and debugging non-deterministic failures • Proficiency in TypeScript or Python, and willingness to work in both: the agent platform is TypeScript on Bun with Temporal workflows on Kubernetes and EC2, the inference platform is Python. • Experience working with modern LLM APIs or open-source models • Experience with or strong interest in model serving (vLLM, TensorRT-LLM, Triton) • Understanding of distributed systems: task queues, event-driven architectures, state management, and durable long-running workflows • Experience with cloud platforms (AWS, GCP) and containerized deployments • Strong understanding of security risks in agentic systems (prompt injection, privilege escalation, data leakage) • Demonstrated experience leading complex technical initiatives • Strong written and verbal communication skills Nice to Have • Experience building agentic systems in regulated industries (fintech, healthcare, enterprise) • Familiarity with Model Context Protocol (MCP) or agent communication standards • Experience with model fine-tuning, quantization, or LoRA • Experience building CI/CD automation and developer tooling • Experience adapting workflow orchestration systems (Temporal, Airflow, Prefect) for AI workloads • Experience with voice models, multimodal models, or edge inference • Experience designing human-in-the-loop or oversight systems 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.

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