{"id":2813157,"url":"https://alion.io/job/coditude-ai-product-engineer","title":"AI Product Engineer","company":{"id":4442740,"name":"Coditude","domain":"coditude.com","url":"https://alion.io/company/coditude","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Keka","truth_index":null},"role":"Backend","role_family":"Backend","seniority":null,"employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Pune, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Claude Code","optional":false},{"name":"LLM","optional":false},{"name":"PostgreSQL","optional":false},{"name":"Amazon EC2","optional":true},{"name":"Anthropic","optional":true},{"name":"Asyncio","optional":true},{"name":"AWS","optional":true},{"name":"Datadog","optional":true},{"name":"FastAPI","optional":true},{"name":"GitHub Actions","optional":true},{"name":"JavaScript","optional":true},{"name":"Least Privilege","optional":true},{"name":"LLM Guardrails","optional":true},{"name":"Model Context Protocol","optional":true},{"name":"Next.js","optional":true},{"name":"OCR","optional":true},{"name":"OpenAI","optional":true},{"name":"Python","optional":true},{"name":"React.js","optional":true},{"name":"Redis","optional":true},{"name":"TypeScript","optional":true},{"name":"Typesense","optional":true}],"status":"live","first_seen_at":"2026-09-03T09:29:54Z","employer_posted_date":"2026-09-03","last_verified_at":"2026-10-18T10:51:57Z","board_verified":true,"closed_at":null,"days_open":45,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":45},"description":"Must-Have Skills:\nStrong software engineering fundamentals in at least one stack - clean APIs, sensible data models, tests, real debugging. Depth in one stack beats familiarity with five.\nLearning agility - a provable track record of entering an unfamiliar domain or codebase and shipping within weeks. This can substitute for most stack requirements.\nAI-native development with judgment - uses Claude Code or equivalent agentic tools as a primary way of building. Should be able to drive one live in the interview and explain where they took the keyboard back.\nHigh agency and builder's bias - takes an ambiguous ask and drives it to a shipped, measured fix.\nPostgreSQL competence - real queries, indexes, transactions, and the instinct that a wrong query is a customer-visible bug.\nClear communication - can explain trade-offs to non-engineers without jargon, writes clearly, and is comfortable with real client contact.\nWorking understanding of LLM agents - should be able to speak to what they've built with an LLM API and how it failed on them. Production-level depth is not required; we teach that.\nGood-to-Have Skills:\nOur environment (depth in some is a plus; candidate ramps on the rest):\nPython 3.11 (FastAPI, asyncio, pydantic)\nAnthropic and OpenAI SDKs\nNext.js / React / TypeScript at working proficiency\nRedis, Typesense\nAWS (EC2/ALB, RDS, SQS, SES)\nGitHub Actions, Datadog\nGenuine bonus:\nProduction LLM/agent work (evals, guardrails, multi-agent, MCP)\nPrior forward-deployed or consulting-style client work\nAirflow or data pipelines\nBrowser automation, OCR/vision\nRoles & Responsibilities:\nBuild and operate AI agents and LLM-powered products - copilots, workflow agents, extraction pipelines, internal tools - and maintain/evolve existing ones.\nOwn problems end to end: from the client conversation through design, code, deploy, and production support.\nWork AI-first, with humans owning judgment and outcomes - validate what the tools produce and own every line that ships.\nTake part in client and partner conversations: discovery, live demos of work in progress, pilot support.\nWrite short proposals and design docs; decisions move through documents and PRs, not meeting chains.\nRun a lane end to end within a small, async-first team.\nWrite tests where they protect what matters - agent behaviour and money paths. 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