{"id":1525854,"url":"https://alion.io/job/qrata-ai-engineer","title":"AI Engineer","company":{"id":3800640,"name":"Qrata","domain":"qrata.co","url":"https://alion.io/company/techmatters-technologies","size_band":null,"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":"senior","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":22000,"max_usd":46000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":29},"experience_years_min":8,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"AutoGen","optional":false},{"name":"CrewAI","optional":false},{"name":"FastAPI","optional":false},{"name":"Knowledge Graph","optional":false},{"name":"LangChain","optional":false},{"name":"LangGraph","optional":false},{"name":"LLM","optional":false},{"name":"Multi-Agent Systems","optional":false},{"name":"Neo4j","optional":false},{"name":"Playwright","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"Python","optional":false},{"name":"Structured Outputs","optional":false},{"name":"Celery","optional":true},{"name":"Redis","optional":true}],"status":"live","first_seen_at":"2026-09-30T12:28:02Z","employer_posted_date":null,"last_verified_at":"2026-09-30T12:28:02Z","board_verified":false,"closed_at":null,"days_open":1,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":1},"description":"Who We Are : \n\nThe infrastructure layer that sits between shipping code and confident releases. We're not a test automation tool. We're the system that understands your application, learns from every test run, and tells engineering teams exactly what's at risk before anything breaks in production.\n\nWhat This Role Is : \n\nYou will be one of the first engineers building the core agent layer of the platform. This is not a role for someone who chains LLM API calls and calls it agentic. The systems you'll build have deterministic rule layers, multi-stage reasoning pipelines, confidence scoring under ambiguity, feedback loops that improve over time, and escalation logic that knows when to stop and ask a human.\n\nWhat You'll Build : \n\n- Multi-agent orchestration systems : agents that plan, execute, self-correct, and escalate.\n\n- Context Graph infrastructure : a living knowledge graph that connects requirements, DOM elements, test cases, code functions, and user behavior signals.\n\n- Browser crawl agents : Playwright-based agents that understand a running application semantically.\n\n- Agentic evaluation frameworks : building the eval layer to verify agent decisions.\n\n- LLM integration at production scale : prompt engineering, structured output, context window management, and confidence calibration.\n\n- Feedback loops : systems that get measurably smarter with every test run.\n\nWhat You Need to Have Done Before : \n\n- Built and shipped multi-agent systems in production.\n\n- Worked with LangGraph, LangChain, CrewAI, AutoGen, or equivalent orchestration frameworks.\n\n- Designed and queried knowledge graphs or graph databases (Neo4j).\n\n- Built systems that detect absence : not just what's wrong, but what's missing.\n\n- Written production Python (async, typed, modular, observable).\n\n- Worked with Playwright, browser-use, or equivalent browser automation.\n\nBackend Tech Stack : \n\nPython (async, typed, strict), FastAPI, Celery, Redis.\n\nHow We'll Evaluate You : \n\nBeyond the standard interview, we give candidates a take-home assignment that we think is the truest signal of the skills this role demands. You'll be asked to build a working agent that crawls a live web application, ingests a product requirements document, constructs a knowledge graph, and uses that graph to reason about the blast radius of a code change.\n\nWhat We Offer : \n\n- Competitive compensation benchmarked to top-of-market.\n\n- Meaningful equity.\n\n- Direct access to the founding team and genuine influence over product direction.\n\n- The chance to be a core architect of a category-defining platform.\n\nSkills\nPython, LangChain, Neo4j, LangGraph, FastAPI, Playwright Testing, Artificial Intelligence, Agentic AI, LLM","description_format":"text","description_chars":2697,"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":["Equity"],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":[],"lifecycle":[{"event":"open","at":"2026-09-30T14:00:00Z"}],"liveness":{"score":86,"band":"hot","label":"Hiring now","p_open":1,"p_active":0.86,"p_room":1,"age_days":0,"expected_fill_days":24,"reasons":["seen:0","win:early"],"computed_at":"2026-10-01T05:45:00Z"},"pay":null,"html_url":"https://alion.io/job/qrata-ai-engineer","json_url":"https://alion.io/job/qrata-ai-engineer.json","meta":{"generated_at":"2026-10-01T12:44:56Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","usage":{"tier":"crawler","counted_by":"address","units_charged":1,"used_today":4185,"day_limit":5000,"remaining_today":815,"minute_limit":60,"resets_at":"2026-10-02T00:00:00Z"}}}