{"id":3167834,"url":"https://alion.io/job/tata-consultancy-services-mlops-engineer","title":"MLOps Engineer","company":{"id":222,"name":"Tata Consultancy Services","domain":"tcs.com","url":"https://alion.io/company/tata-consultancy-services","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Career site","truth_index":{"grade":"A","score":100,"open_postings":124,"ghost_share":0.008,"stale_share":0,"repost_share":0.032,"time_to_fill_p50_days":4,"computed_at":"2026-11-02T06:13:15Z"}},"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","Hyderabad, India","Pune, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":21000,"max_usd":43000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":41},"experience_years_min":8,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Azure","optional":false},{"name":"CI/CD","optional":false},{"name":"Databricks","optional":false},{"name":"GCP","optional":false},{"name":"GitHub Actions","optional":false},{"name":"Machine Learning","optional":false},{"name":"MLFlow","optional":false},{"name":"pySpark","optional":false},{"name":"Terraform","optional":false},{"name":"Python","optional":true},{"name":"Spark","optional":true}],"status":"live","first_seen_at":"2026-10-23T17:36:00Z","employer_posted_date":null,"last_verified_at":"2026-10-23T17:36:00Z","board_verified":false,"closed_at":null,"days_open":10,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":10},"description":"The client currently operates data science workloads in GCP and is migrating these workloads to Azure Databricks. Input data will continue to originate from GCP, data science processing will execute in Azure Databricks, and outputs will be written back to GCP. The MLOps engineer will play a critical role in model migration, workflow automation, CI/CD implementation, model lifecycle management, and cross-cloud data movement initiatives.\n\nResponsibilities: \nSupport migration of existing ML models from GCP to Azure Databricks.\nAnalyse, replicate, and optimise existing data science model architectures.\nBuild and maintain CI/CD pipelines using GitHub Actions.\nImplement and manage MLflow for model tracking, versioning, and lifecycle management.\nDevelop scalable data & ML pipelines using Databricks and PySpark.\nCollaborate with data engineering teams on GCP-to-Azure Databricks data movement strategies.\nBuild pipelines to move model outputs from Azure Databricks back to GCP.\nProvide architectural guidance and workflow optimisation recommendations.\nImprove testing coverage, monitoring, observability, and performance tuning.\nDrive cost optimisation and operational efficiency initiatives.\n\nRequirements: \nStrong Azure Databricks expertise is mandatory.\nHands-on experience with PySpark, MLflow, and GitHub Actions is required.\nCandidates must have exposure to both Azure and GCP ecosystems.\nExperience in ML model migration, operationalisation, and optimisation is highly preferred.\nStrong troubleshooting, performance tuning, and cost optimisation capabilities are essential.\n\nMandatory Technical Skills: \nStrong hands-on expertise in Azure Databricks with a deep understanding of Databricks internals.\nAdvanced PySpark development, optimisation, and execution plan analysis.\nExperience building CI/CD pipelines using GitHub Actions.\nStrong experience with MLflow for model tracking, deployment, and lifecycle management.\nKnowledge of Terraform and Databricks infrastructure automation.\nExperience integrating workflows across GCP and Azure cloud platforms.\nStrong debugging, troubleshooting, and performance optimisation capabilities.\nExperience operationalising machine learning models in production environments.\nCost optimisation mindset for cloud and data processing workloads.\n\nGood to Have: \nExperience with cross-cloud data movement architectures.\nUnderstanding of data science model structures and workflows.\nExposure to model monitoring, observability, and alerting frameworks.\nExperience collaborating closely with data science teams.\nSkills\nDatabricks, ML Ops, MLOps, PySpark","description_format":"text","description_chars":2596,"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":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["IT Consulting & Digital Transformation","IT Outsourcing & Dedicated Teams","Systems Integrators","Management Consulting"],"lifecycle":[{"event":"open","at":"2026-10-23T17:36:00Z"}],"visa":[],"liveness":{"score":19,"band":"cold","label":"Long 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