{"id":1279109,"url":"https://alion.io/job/talentone-in-ai-observability-engineer","title":"AI Observability Engineer","company":{"id":3800811,"name":"TalentOne.in","domain":"talentone.co.in","url":"https://alion.io/company/talentone-in","size_band":null,"is_staffing_agency":true,"employer_type":"agency","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":["Pune, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":20000,"max_usd":42000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":27},"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Anomaly Detection","optional":false},{"name":"Azure","optional":false},{"name":"CI/CD","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Scikit-learn","optional":false},{"name":"TensorFlow","optional":false}],"status":"live","first_seen_at":"2026-08-26T08:24:38Z","employer_posted_date":null,"last_verified_at":"2026-08-26T08:24:38Z","board_verified":false,"closed_at":null,"days_open":48,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":48},"description":"Position Overview:\n\nThe AI Observability Engineer will be instrumental in implementation of scalable, cloud-native solutions to meet the growing needs of our Data & Development team. 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The AI Observability Engineer will build and maintain a robust framework to ensure the reliability and maintainability of DPR Construction's complex AI systems.\n\nResponsibilities:\n\n- Standardize observability practices across AI/ML and other development teams including logging, metrics, tracing, and model performance monitoring, ingesting data from multiple platforms.\n\n- Lead hands-on implementation of automation-first DevOps and MLOps practices, enabling infrastructure-as-code and consistent, repeatable environment provisioning.\n\n- Design and manage intelligent DataOps pipelines with automated data quality monitoring and anomaly detection.\n\n- Deploy, maintain and monitor containerized ML workloads.\n\n- Extend existing CI/CD pipelines to support automated infrastructure changes and ML workflows.\n\n- Implement AI-driven data validation, schema and concept drift detection and metadata management.\n\n- Establish governance frameworks for AI systems, including bias detection, 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