{"id":188604,"url":"https://alion.io/job/zenskar-staff-genai-engineer","title":"Staff GenAI Engineer","company":{"id":2844,"name":"Zenskar","domain":"zenskar.com","url":"https://alion.io/company/zenskar","size_band":"51-200","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":null,"truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"staff","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Bengaluru, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":33000,"max_usd":83000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":10},"experience_years_min":8,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Embeddings","optional":false},{"name":"Fine-tuning","optional":false},{"name":"Function Calling","optional":false},{"name":"Hallucination","optional":false},{"name":"Human-in-the-Loop","optional":false},{"name":"LLM","optional":false},{"name":"LLM Evaluation","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"RAG","optional":false},{"name":"Structured Outputs","optional":false},{"name":"CrewAI","optional":true},{"name":"LangChain","optional":true},{"name":"LangGraph","optional":true},{"name":"Model Context Protocol","optional":true},{"name":"Multimodal AI","optional":true},{"name":"OpenAI Agents SDK","optional":true},{"name":"Pydantic AI","optional":true}],"status":"live","first_seen_at":"2026-09-02T05:33:19Z","employer_posted_date":null,"last_verified_at":"2026-09-02T05:33:19Z","board_verified":false,"closed_at":null,"days_open":46,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":46},"description":"This role sets the standard for how Gen AI gets built into Zenskar's product. You're accountable for whether the Gen AI features Zenskar ships are reliable, safe, and cost-sane in production, not for whether any single feature gets built. Concretely, this role exists so that no team ever ships an LLM integration that hallucinates a wrong invoice amount or burns unbounded API costs, because there was no standard pattern to build against.\nThis is an IC role with org-wide influence, not team ownership. You'll code on the critical paths where it matters most, but your leverage comes from setting patterns other teams build on top of, not from personally shipping every Gen AI feature. Please think 1-3 years for how Gen AI gets integrated across the product, not one team's roadmap. What this role is not: foundation model training, deep RAG/retrieval specialization as a primary mandate, fine-tuning specialization, or MLOps infrastructure ownership. Those are adjacent lanes. This is LLM application engineering: building real product features on top of frontier model APIs and making them production-reliable.\nResponsibilities:\nTurn ambiguous product asks into working LLM-based features: prompt and context design, tool calling, orchestration across model calls, and structured output handling.\nDesign for the failure modes non-deterministic systems actually have: hallucination, drift, and silent wrongness. Build guardrails, and know the difference between demo-grade and production-grade because you've filled the gap between them.\nMake sure an LLM is never the unverified source of truth for a financial decision. Design the verification or human-in-the-loop layer wherever money is actually at stake.\nPick the right model or vendor for the job (cost, quality, latency, data policy) and avoid lock-in. Once chosen, run it efficiently: token economics, caching, batching, and per-request cost tracking.\nSet the technical standard other teams build their Gen AI features against, and get multiple teams actually to adopt it without having direct authority over them.\nBuild and own the eval harness that catches quality regressions before they ship, not ad hoc spot-checks.\nKnow when not to use an LLM at all, where a deterministic system would be cheaper, more reliable, or simply correct. Name and quantify technical debt in Gen AI systems (prompt sprawl, eval gaps, untracked cost, brittle integrations) and negotiate time to address it.\nShape who joins the team by holding a technical bar in interview loops, without owning headcount decisions.\nRequirements:\n8+ years of software engineering experience, including meaningful experience shipping AI-powered products to production.\nA real, shipped-at-scale system in your track record doesn't have to be Gen AI. If it's ML work, it shipped to real users, not fine-tuning or research that stayed in a notebook.\nDeep understanding of LLM application architecture: tool use, structured outputs, retrieval, orchestration, and where these actually break in production.\nExperience building agentic systems that do multi-step reasoning and interact with tools or business systems.\nA track record of treating prompts as versioned, testable, deployable artifacts, not strings scattered through the codebase.\nStrong RAG fundamentals: retrieval quality, chunking, embeddings, evaluation, and knowledge system design.\nExperience designing evaluation frameworks and regression pipelines for AI systems.\nStrong grasp of AI observability: tracing, monitoring, feedback loops, and production debugging.\nA track record of getting more than one team to adopt a pattern or standard you set, without owning those teams.\nJudgment for when not to use an LLM at all.\nStrong backend engineering skills and enough frontend ability to own an AI experience end to end.\nCan clearly walk through an AI system you've built two levels below the pitch: what failed, what you learned, and how you improved reliability over time.\nGood-to-Have:\nMemory architectures: episodic memory, procedural memory, retrieval systems, and knowledge stores.\nAgent orchestration frameworks (LangGraph, Pydantic AI, OpenAI Agents SDK, CrewAI, or a custom runtime).\nLong-running autonomous workflows and event-driven agent systems.\nVoice, multimodal, or real-time AI systems.\nFine-tuning experience and understanding of model internals beyond API consumption.\nOpen-source model deployment and inference infrastructure.\nExperience with financial systems, billing platforms, revenue operations, accounting, or fintech.\nFamiliarity with MCP, tool ecosystems, and AI platform architecture.\nStartup experience and comfort operating with high ownership and no formal authority.","description_format":"text","description_chars":4679,"description_truncated":false,"requirements":{"experience_years_min":8,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Billing & Subscription Management","Accounting & Tax Software"],"lifecycle":[{"event":"open","at":"2026-09-02T05:33:19Z"}],"visa":[],"liveness":{"score":16,"band":"cold","label":"Long shot","p_open":0.4,"p_active":0.706,"p_room":0.55,"age_days":45,"expected_fill_days":36,"reasons":["seen:45","win:tail"],"computed_at":"2026-10-17T05:46:00Z"},"pay":null,"html_url":"https://alion.io/job/zenskar-staff-genai-engineer","json_url":"https://alion.io/job/zenskar-staff-genai-engineer.json","meta":{"generated_at":"2026-10-18T20:52:59Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","about":"Alion is a live layer of people, companies and AI agents: who they are, whether they are real and active right now, what they do and how to work with them, readable by people and by agents and paid per call.","catalog":"https://alion.io/catalog.json","usage":{"tier":"crawler_verified","counted_by":"address","units_charged":1,"used_today":24603,"day_limit":null,"remaining_today":null,"minute_limit":300,"resets_at":"2026-10-19T00:00:00Z"}},"offers":[{"id":"company.slices","title":"One company in depth, by slice","status":"live","price":{"credits":0.02,"usd":0.002,"plus_per_slice":{"credits":0.05,"usd":0.005}},"unit":"per company, plus each slice with data","note":"the employer in depth","call":{"mcp_tool":"get_company","arguments":{"id":2844},"rest":"https://alion.io/mcp/rest/get_company?id=2844"},"human":"https://alion.io/catalog?offer=company.slices&for=job%2Fzenskar-staff-genai-engineer"},{"id":"market.stats","title":"A market slice: pay, demand and time to fill","status":"live","price":{"credits":1,"usd":0.1},"unit":"per slice","note":"pay, demand and time to fill for this role and place","call":{"mcp_tool":"market_stats"},"human":"https://alion.io/catalog?offer=market.stats&for=job%2Fzenskar-staff-genai-engineer"},{"id":"job.search","title":"Open jobs by role, technology, place, pay and visa","status":"live","price":{"credits":0.02,"usd":0.002},"unit":"per posting in a list","note":"similar open postings","call":{"mcp_tool":"search_jobs"},"human":"https://alion.io/catalog?offer=job.search&for=job%2Fzenskar-staff-genai-engineer"},{"id":"company.verify","title":"Is this company real and active right now","status":"pilot","price":null,"unit":"per company","request":{"url":"https://alion.io/catalog/request","method":"POST","body":"{\"offer\": \"company.verify\", \"for\": \"job/zenskar-staff-genai-engineer\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Fzenskar-staff-genai-engineer"}]}