{"id":3782898,"url":"https://alion.io/job/tekion-eval-engineer","title":"Eval Engineer","company":{"id":804,"name":"Tekion","domain":"tekion.com","url":"https://alion.io/company/tekion","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Ashby","truth_index":{"grade":"B","score":82,"open_postings":17,"ghost_share":0,"stale_share":0.706,"repost_share":0,"time_to_fill_p50_days":47,"computed_at":"2026-11-06T05:58:05Z"}},"role":"Industrial Engineering","role_family":"Industrial Engineering","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","Chennai, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Java","optional":false},{"name":"LLM","optional":false},{"name":"Python","optional":false},{"name":"SLI/SLO/SLA","optional":false}],"status":"live","first_seen_at":"2026-11-05T01:35:45Z","employer_posted_date":null,"last_verified_at":"2026-11-05T01:35:45Z","board_verified":false,"closed_at":null,"days_open":1,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":1},"description":"Responsibilities: \nDesign and own evaluation systems for agent domains.\nTake architectural responsibility for eval coverage across one or more capability domains: multi-turn conversation evals, tool-selection accuracy, goal completion, policy compliance, and safety boundary probing.\nCoverage spans behavioural equivalence, SLOs, idempotency, chaos-as-test, context and retrieval quality, and tool/dependency failure modes; each is a distinct failure surface that functional correctness evals alone do not cover.\nDefine what \"evaluated\" means for the domain, not just execute a checklist.\n\nRequirements: \n5+ years of software engineering experience, with at least 2 years building evaluation, measurement, or quality systems for AI/ML products.\nEngineering-parallel fundamentals: you can design, build, and operate evaluation infrastructure as production-grade distributed systems (services, pipelines, storage, observability), not scripts and spreadsheets.\nDeep hands-on LLM evaluation experience: LLM-as-judge design and calibration, human annotation pipelines, golden dataset governance, and systematic error analysis from raw traces.\nDemonstrated ability to define acceptance criteria for nondeterministic behaviour, turning fuzzy notions of quality into thresholds an organisation can operate against.\nExperience diagnosing agentic systems across the full execution path: able to attribute a failure to prompt, context, tooling, or model capability, and defend the attribution with evidence.\nExperience wiring evaluation into deployment flows as a hard gate, blocking bad changes before they reach production rather than reporting on them after.\nStatistical fluency applied to evaluation: sample size design, confidence intervals, and significance testing, enough to defend a regression call when a team disputes it.\nProficiency in Python and/or Java at the level needed to build and maintain a shared eval framework others depend on.\nExperience operating in environments where AI failures have real business or compliance consequences.\nSkills\nAgent, Agentic AI, Evaluation, LLM, Large Language Model, 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