{"id":644939,"url":"https://alion.io/job/paytm-payments-services-staff-ai-platform-engineer-inference-agentic-systems","title":"Staff AI Platform Engineer - Inference & Agentic Systems","company":{"id":4340,"name":"Paytm","domain":"paytm.com","url":"https://alion.io/company/paytm-payments-services","size_band":"501-1000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Lever","truth_index":{"grade":"B","score":75,"open_postings":44,"ghost_share":0,"stale_share":0.818,"repost_share":0,"time_to_fill_p50_days":60,"computed_at":"2026-10-26T05:57:25Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"staff","employment_type":"full_time","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Toronto, Canada"],"countries":["CA"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":95000,"max_usd":196000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":23},"experience_years_min":8,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Amazon EC2","optional":false},{"name":"AWS","optional":false},{"name":"Function Calling","optional":false},{"name":"GCP","optional":false},{"name":"Human-in-the-Loop","optional":false},{"name":"Kubernetes","optional":false},{"name":"LLM","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"Python","optional":false},{"name":"Quantization","optional":false},{"name":"Temporal","optional":false},{"name":"TensorRT-LLM","optional":false},{"name":"Triton","optional":false},{"name":"TypeScript","optional":false},{"name":"vLLM","optional":false},{"name":"CI/CD","optional":true},{"name":"Edge AI","optional":true},{"name":"Fine-tuning","optional":true},{"name":"Go","optional":true},{"name":"LoRA","optional":true},{"name":"Model Context Protocol","optional":true},{"name":"Multimodal AI","optional":true},{"name":"PEFT","optional":true},{"name":"Prefect","optional":true},{"name":"TensorRT","optional":true}],"status":"live","first_seen_at":"2026-04-16T18:32:49Z","employer_posted_date":"2026-04-16","last_verified_at":"2026-10-26T08:29:11Z","board_verified":true,"closed_at":null,"days_open":192,"trust":{"level":"stale","repost_count":0,"flags":["stale"],"days_open":192},"description":"About the Role\nWe are a small team of AI builders in Paytm Labs.\nAs a Staff AI Platform Engineer, you will work across inference and agentic systems. You will\ncontribute to Paytm's AI inference platform (Pi), serving internal teams and enterprise customers\n- running our own coding and domain-specific models (voice, vision, risk, fintech workflows) as\nwell as third-party models. You will also architect and build the platform that enables\nautonomous AI agents to operate safely and reliably in production - the runtime, orchestration,\nand developer tooling for agents to reason, plan, use tools, and execute complex multi-step\nworkflows, automating both software development and business processes.\nYou will work at the intersection of LLMs, distributed systems, and production fintech\ninfrastructure, helping define how inference and agentic AI are built and deployed across\npayments, risk, fraud, collections, support, and developer experience.\nWhat You'll Do\n Inference & Model Serving\nBuild and operate multi-model serving across modalities (text, voice, code, vision) on shared infrastructure\nOwn the model lifecycle: download, deploy, serve, monitor, update, swap\nDrive inference optimization: latency, throughput, cost - including quantization, batching, caching, and routing strategies\nEnsure inference is fast and reliable for the agents and systems that depend on it\nAgentic Systems\nArchitect and build the Agentic AI Platform - runtime infrastructure, orchestration systems, and developer tooling for autonomous agents\nDesign multi-agent coordination systems enabling agents to collaborate and solve complex workflows\nBuild robust tool-use infrastructure that allows agents to interact with APIs, databases, and services safely\nImplement workflow automation: agents that execute multi-step business and engineering tasks with appropriate guardrails\nBuild safety and guardrail systems including permissioning, sandboxing, and human-in-the-loop workflows\nDevelop evaluation and observability frameworks to measure agent behaviour, detect regressions, and debug failures\nDevelop SDKs and APIs that allow internal teams to build and deploy agents quickly and safely\n Platform & Technical Leadership\nDefine technical direction and architecture for agentic systems across the organization\nBuild patterns and standards for agent design, tool calling, and evaluation\nPartner closely with ML, product, and security teams to deliver production-grade agent systems\nMentor engineers and contribute to best practices for agent system design\nWhat You'll Bring\n8+ years of software engineering experience, with 3+ years in AI systems or LLM applications\nStrong understanding of LLM-based agent architectures: tool use, multi-step workflows, multi-agent coordination, and their failure modes\nExperience building highly reliable distributed systems\nExperience evaluating LLM systems in production: building evals, detecting regressions, and debugging non-deterministic failures\nProficiency 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. \nExperience working with modern LLM APIs or open-source models \nExperience with or strong interest in model serving (vLLM, TensorRT-LLM, Triton)\n Understanding of distributed systems: task queues, event-driven architectures, state management, and durable long-running workflows \nExperience with cloud platforms (AWS, GCP) and containerized deployments\nStrong understanding of security risks in agentic systems (prompt injection, privilege escalation, data leakage) \nDemonstrated experience leading complex technical initiatives\nStrong written and verbal communication skills \nNice to Have\nExperience building agentic systems in regulated industries (fintech, healthcare, enterprise) \nFamiliarity with Model Context Protocol (MCP) or agent communication standards\nExperience with model fine-tuning, quantization, or LoRA \nExperience building CI/CD automation and developer tooling \nExperience adapting workflow orchestration systems (Temporal, Airflow, Prefect) for AI workloads \nExperience with voice models, multimodal models, or edge inference \nExperience designing human-in-the-loop or oversight 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