{"id":3002753,"url":"https://alion.io/job/azisly-applied-ai-ml-engineer","title":"Applied AI / ML Engineer","company":{"id":47001,"name":"Azisly","domain":"azisly.ai","url":"https://alion.io/company/azisly","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":"middle","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Gurgaon, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":19000,"max_usd":51000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":31},"experience_years_min":4,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Amazon S3","optional":false},{"name":"Docker","optional":false},{"name":"Hugging Face","optional":false},{"name":"Interpretability","optional":false},{"name":"LLM","optional":false},{"name":"LLM Evaluation","optional":false},{"name":"Oracle","optional":false},{"name":"PostgreSQL","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Scikit-learn","optional":false},{"name":"SQL","optional":false},{"name":"AI Agents","optional":true},{"name":"EU AI Act","optional":true},{"name":"Machine Learning","optional":true},{"name":"Multimodal AI","optional":true},{"name":"Speech Recognition","optional":true}],"status":"live","first_seen_at":"2026-10-21T12:35:00Z","employer_posted_date":null,"last_verified_at":"2026-10-21T12:35:00Z","board_verified":false,"closed_at":null,"days_open":3,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":3},"description":"You will build the first version of hiring intelligence models, the system that turns recorded interview transcripts into evidence-backed candidate profiles and explainable, ranked shortlists that real recruiters act on. This is an applied ML role, not a research role. The center of gravity is LLM-based structured extraction done with engineering discipline, a transparent matching/scoring model, and the evaluation harness that proves (or disproves) that any of it works. You will own this pipeline end-to-end from raw transcript to number-in-database and ship it to production. We operate on one hard rule: LLMs extract evidence; they never make the final prediction. Every score the system produces must decompose to evidence a recruiter or auditor can inspect. If that constraint excites you rather than annoys you, you're our profile.\n\nResponsibilities: \nBuild the extraction pipeline: LLM-based structured extraction of competency evidence from interview transcripts, versioned prompts and rubrics, persisted outputs with provenance, evidence quotes, and measured accuracy against expert labels.\nTreated as an engineering system, not prompt tinkering.\nBuild the v1 scoring/matching model: a transparent, requirement-weighted scoring model that ranks candidates against role requirements. Interpretability is a hard constraint. Lay the groundwork for learning-to-rank as recruiter-preference data accumulates.\nBuild the evaluation harness: precision@K against recruiter picks, agreement with expert labels, calibration where used, and subgroup fairness checks on ranked outcomes. Every model version ships with an evaluation report.\nRun label operations: design and run the expert-labelling loop with our SMEs and the recruiter preference capture; own training-data snapshots and model versioning.\nShip to production: Own the full lifecycle of prototyping, evaluation, deployment, and monitoring, working with Python, Postgres, S3 and standard job-queue/batch infrastructure.\nWork independently: take ambiguous problems, figure out the right approach, propose it in writing, and drive it to production with minimal hand-holding.\n\nWhat Success Looks Like: \n30 days: extraction pipeline producing versioned, evidence-linked outputs on real interview transcripts; evaluation scaffold running.\n60 days: v1 requirement-weighted scoring model live behind a shadow run; expert-label loop in motion; first accuracy and fairness report.\n90 days: a written, statistically honest read on model quality against recruiter picks, including what to keep, what to fix, and what to kill.\n\nRequirements: \n4-6 years of hands-on experience building and shipping ML/AI products to production and keeping them running. Not just research or notebooks.\nLLM-as-component discipline: you have built structured-extraction or LLM-pipeline systems with versioned prompts and measured accuracy; you treat LLM output as a data source to be evaluated, not an oracle.\nClassical ML fundamentals: linear/gradient-boosted models, regularization, honest train/test hygiene; you know what leaks and what overfits when you have only a few hundred labels.\nEvaluation literacy: you can design an experiment, argue precision@K vs AUC vs calibration, and you are willing to say \"this doesn't work\" when the numbers say so.\nStrong Python and the modern ML stack (PyTorch / scikit-learn / Hugging Face or equivalent), plus comfort with data plumbing (SQL, batch pipelines, Docker).\nA builder's mindset with proven independent ownership; you've shipped real products end to end.\n\nBonus Points: \nLearning-to-rank or recommender-system experience.\nPrior hiring-tech or assessment-tech exposure, or awareness of its regulatory landscape (NYC LL144 EU AI Act).\nFairness/bias measurement experience on ranked or scored outcomes.\nSpeech/ASR or multimodal (audio/video) feature-extraction experience: this is our roadmap, not the day-one job.\nVector search / embedding-retrieval experience.\nEarly-stage startup or founder-led environment experience.\nSkills\nPython, LLMs, Machine Learning, PyTorch, Scikit-learn, Agentic AI, Generative AI","description_format":"text","description_chars":4091,"description_truncated":false,"requirements":{"experience_years_min":4,"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":["Career Services","HR & Recruiting AI"],"lifecycle":[{"event":"open","at":"2026-10-21T12:35:00Z"}],"visa":[],"liveness":{"score":90,"band":"hot","label":"Hiring now","p_open":1,"p_active":0.903,"p_room":1,"age_days":2,"expected_fill_days":23,"reasons":["seen:2","velocity","win:early"],"computed_at":"2026-10-24T06:02:35Z"},"pay":null,"html_url":"https://alion.io/job/azisly-applied-ai-ml-engineer","json_url":"https://alion.io/job/azisly-applied-ai-ml-engineer.json","meta":{"generated_at":"2026-10-24T22:02:45Z","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":32282,"day_limit":null,"remaining_today":null,"minute_limit":300,"resets_at":"2026-10-25T00:00:00Z"}},"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":47001},"rest":"https://alion.io/mcp/rest/get_company?id=47001"},"human":"https://alion.io/catalog?offer=company.slices&for=job%2Fazisly-applied-ai-ml-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%2Fazisly-applied-ai-ml-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%2Fazisly-applied-ai-ml-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/azisly-applied-ai-ml-engineer\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Fazisly-applied-ai-ml-engineer"}]}