{"id":1227220,"url":"https://alion.io/job/hexacorp-mlops-engineer","title":"MLOps Engineer","company":{"id":3800378,"name":"HexaCorp","domain":"hexacorp.com","url":"https://alion.io/company/hexacorp","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":"middle","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":21000,"max_usd":53000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":22},"experience_years_min":4,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Azure","optional":false},{"name":"CI/CD","optional":false},{"name":"Databricks","optional":false},{"name":"Delta Lake","optional":false},{"name":"Docker","optional":false},{"name":"Feature Store","optional":false},{"name":"Kubernetes","optional":false},{"name":"Machine Learning","optional":false},{"name":"MLFlow","optional":false},{"name":"Python","optional":false},{"name":"SQL","optional":false}],"status":"live","first_seen_at":"2026-09-25T08:13:13Z","employer_posted_date":null,"last_verified_at":"2026-09-25T08:13:13Z","board_verified":false,"closed_at":null,"days_open":4,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":4},"description":"What you will be doing (responsibilities) :\n\n1. Model Deployment & CI/CD :\n\n- Build and maintain CI/CD pipelines for ML model packaging, testing, and deployment across dev, test, and production environments.\n\n- Support containerization and orchestration of model services using standard platform tooling.\n\n- Implement controlled release patterns (staged rollouts, rollback procedures) for model updates.\n\n- Contribute to reusable deployment templates and pipeline patterns that reduce rework across model teams.\n\n2. Monitoring & Observability :\n\n- Implement monitoring for model performance, data drift, and pipeline health in production.\n\n- Set up alerting and dashboards to flag degraded model accuracy, latency issues, or job failures.\n\n- Support root-cause investigation of production incidents and contribute to post-incident fixes.\n\n- Maintain logging and traceability so model behavior can be audited and reproduced.\n\n3. Pipeline & Infrastructure Support :\n\n- Operate and maintain training, retraining, and batch-scoring pipelines on schedule.\n\n- Manage model registry entries, versioning, and artifact lineage for deployed models.\n\n- Support environment hygiene, including dependency management and base image updates.\n\n- Partner with platform teams to ensure efficient use of compute resources for training and inference.\n\n4. Collaboration & Enablement :\n\n- Work with Data Scientists and ML Engineers to translate model requirements into deployable services.\n\n- Partner with Data Engineering to ensure consistent, reliable data feeds into ML pipelines.\n\n- Document deployment patterns, runbooks, and operational standards to support team self-service.\n\n- Communicate clearly on deployment status, risks, and dependencies to stakeholders.\n\nWhat you bring (Qualifications) :\n\nRequired :\n\n- 4 - 7 years of hands-on experience in MLOps, ML engineering, or DevOps roles with exposure to machine learning workflows.\n\n- Working knowledge of CI/CD tooling and practices applied to model deployment.\n\n- Experience with containerization (Docker) and orchestration concepts (Kubernetes or equivalent).\n\n- Proficiency in Python and SQL, with the ability to script and automate operational tasks.\n\n- Familiarity with cloud platforms (Azure preferred) and their ML services.\n\nPreferred :\n\n- Exposure to model registry, experiment tracking, or feature store tools (MLflow, Databricks, or equivalent).\n\n- Experience with monitoring/observability tooling for data or ML workloads.\n\n- Background in retail, consumer goods, or other data-intensive industries.\n\n- Familiarity with Databricks and Delta Lake-based environments.\nSkills\nMachine Learning, MLOps, CI/CD Pipeline, CI/CD Tools, Python, SQL, Docker, Kubernetes, Databricks","description_format":"text","description_chars":2720,"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":["IT Consulting & Digital Transformation","AI Consulting & Integration","Cloud Consulting & Migration"],"lifecycle":[{"event":"open","at":"2026-09-25T13:06:44Z"}],"liveness":{"score":95,"band":"hot","label":"Hiring now","p_open":1,"p_active":0.946,"p_room":1,"age_days":3,"expected_fill_days":38,"reasons":["seen:3","urgency","win:early"],"computed_at":"2026-09-29T05:45:00Z"},"pay":null,"html_url":"https://alion.io/job/hexacorp-mlops-engineer","json_url":"https://alion.io/job/hexacorp-mlops-engineer.json","meta":{"generated_at":"2026-09-30T04:38:04Z","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":3332,"day_limit":5000,"remaining_today":1668,"minute_limit":60,"resets_at":"2026-10-01T00:00:00Z"}}}