{"id":4164114,"url":"https://alion.io/job/symphoni-hr-databricks-mlops-engineer","title":"Databricks MLOps Engineer","company":{"id":3800108,"name":"Symphoni HR","domain":"symphonihr.com","url":"https://alion.io/company/symphoni-hr","size_band":"201-500","is_staffing_agency":true,"employer_type":"agency","is_intermediary":false,"listed_via":null,"ats_vendor":"Schema","truth_index":{"grade":"A","score":93,"open_postings":43,"ghost_share":0,"stale_share":0,"repost_share":0.465,"time_to_fill_p50_days":45,"computed_at":"2026-11-12T10:38:37Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":"full_time","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":null,"experience_years_min":6,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agile","optional":false},{"name":"Amazon SageMaker","optional":false},{"name":"Azure","optional":false},{"name":"Azure DevOps","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":"GitHub Actions","optional":false},{"name":"Kubernetes","optional":false},{"name":"LLM","optional":false},{"name":"Machine Learning","optional":false},{"name":"MLFlow","optional":false},{"name":"pySpark","optional":false},{"name":"Python","optional":false},{"name":"Rest API","optional":false},{"name":"Scrum","optional":false},{"name":"Spark","optional":true}],"status":"live","first_seen_at":"2026-11-12T13:04:20Z","employer_posted_date":"2026-11-12","last_verified_at":"2026-11-12T20:33:15Z","board_verified":true,"closed_at":null,"days_open":0,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":0},"description":"The Databricks MLOps Engineer will take machine learning models from notebooks to tested, monitored production systems. You will build feature pipelines, automate training and retraining, manage model deployment and serving, and implement CI/CD for ML. The role also supports LLM and GenAI workloads with clear cost controls, working closely with data scientists and product teams.\nOur client is a leading global technology and analytics firm operating in the computer software industry. They serve enterprises worldwide with data-driven products and services built on modern cloud and AI platforms. The organization is known for its scale, its focus on innovation, and a collaborative culture that values engineering excellence.\nResponsibilities\nBuild feature pipelines on Delta tables and manage features in Unity Catalog feature tables.\nAutomate training and retraining with parameterized, scheduled Databricks Workflows.\nTrack experiments and register, version and promote models using MLflow and the Unity Catalog model registry, including champion and challenger aliases.\nDeploy models to Databricks Model Serving for real-time inference and batch jobs, running A/B and shadow deployments.\nMonitor models with Lakehouse Monitoring and inference tables, covering drift, latency, error rates and alert thresholds.\nImplement CI/CD for ML with Databricks Asset Bundles and GitHub Actions or Azure DevOps, including tests, staged promotion and rollback.\nSupport LLM and GenAI serving endpoints, AI Gateway rate limits, model-tier selection and token usage tracking.\nPartner with data scientists to convert notebooks into tested, packaged production code.\nRequirements\n6+ years of experience in ML engineering, MLOps or data engineering with ML delivery.\n3+ years of hands-on experience building ML pipelines on Databricks, including Workflows, MLflow, Model Serving or batch inference.\nStrong production Python skills with PySpark and Delta Lake.\nExperience with multiple models deployed to production with monitoring, retraining and rollback in place.\nBachelor's or Master's degree in Computer Science, Data Science, Engineering or a related field.\nAbility to explain model behaviour, drift and risk to business stakeholders in plain language.\nExperience in Agile/Scrum environments with clear documentation of pipelines and runbooks.\nSkills\nDatabricks Lakehouse Platform, MLflow, Python, PySpark, Delta Lake, Databricks Workflows, Model Serving, Lakehouse Monitoring, Databricks Asset Bundles, CI/CD with GitHub Actions or Azure DevOps, Feature Store, Unity Catalog feature tables, Docker, REST APIs, Kubernetes, Azure Machine Learning, Amazon SageMaker, Vector Search, Databricks Certified Machine Learning Associate or Professional","description_format":"text","description_chars":2736,"description_truncated":false,"requirements":{"experience_years_min":6,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"bachelor","optional":false},"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":[],"lifecycle":[{"event":"open","at":"2026-11-12T13:04:20Z"}],"visa":[],"liveness":{"score":52,"band":"ok","label":"Likely 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