{"id":1332335,"url":"https://alion.io/job/ecolab-ai-engineer-data-databricks-pltform","title":"AI Engineer-Data Databricks Pltform","company":{"id":1754712,"name":"Ecolab","domain":"ecolab.com","url":"https://alion.io/company/ecolab-com","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":{"grade":"B","score":75,"open_postings":104,"ghost_share":0,"stale_share":1,"repost_share":0,"time_to_fill_p50_days":25,"computed_at":"2026-10-01T05:45:00Z"}},"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":["Bengaluru, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":26000,"max_usd":55000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":29},"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Apache Kafka","optional":false},{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"Azure Data Factory","optional":false},{"name":"Azure DevOps","optional":false},{"name":"CI/CD","optional":false},{"name":"Databricks","optional":false},{"name":"Delta Lake","optional":false},{"name":"Feature Store","optional":false},{"name":"GCP","optional":false},{"name":"GitHub Actions","optional":false},{"name":"Incident Management","optional":false},{"name":"Machine Learning","optional":false},{"name":"MLFlow","optional":false},{"name":"Oracle","optional":false},{"name":"Platform Engineering","optional":false},{"name":"pySpark","optional":false},{"name":"Python","optional":false},{"name":"Snowflake","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false},{"name":"Terraform","optional":false},{"name":"AI Agents","optional":true},{"name":"Docker","optional":true},{"name":"Kubernetes","optional":true},{"name":"LLM","optional":true}],"status":"live","first_seen_at":"2026-09-21T00:00:00Z","employer_posted_date":"2026-09-21","last_verified_at":"2026-10-01T03:59:05Z","board_verified":true,"closed_at":null,"days_open":10,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":10},"description":"Job Characteristics: Independently design, implement, and deploy intelligent systems powered by large language models (LLMs), agent orchestration frameworks, and embedding-based retrieval solutions. This role blends hands-on engineering with solution thinking - focusing on scalable workflows, cloud-native delivery, and real-world GenAI application integration.\nEducation/Work Experience: Degree and 2-4 years experience.\nIndependence Level/Reports to: Normally reports to AI Engineering Manager.\n\nAdditional Job Description\nJob Description: Databricks Platform Engineer\nPosition Summary\nWe are seeking a highly skilled Databricks Platform Engineer to design, build, configure, integrate, and operationalize enterprise Data & AI solutions on the Databricks platform. This role will be responsible for establishing scalable, secure, and reliable data and AI capabilities, enabling data engineers, data scientists, AI engineers, and business teams to accelerate innovation and deliver business value.\nThe ideal candidate will possess deep expertise in Databricks, cloud platforms, data engineering, MLOps, platform automation, and enterprise integration patterns.\nKey Responsibilities\nPlatform Engineering & Administration\nDesign, build, configure, and maintain Databricks workspaces across development, testing, and production environments.\nImplement platform standards, reusable frameworks, templates, and best practices.\nManage Unity Catalog, clusters, SQL Warehouses, compute policies, workspace configurations, and access controls.\nAutomate platform deployment and configuration using Infrastructure as Code (Terraform, CI/CD pipelines).\nData Engineering & Integration\nBuild and integrate scalable data pipelines using Databricks Lakehouse architecture.\nDesign ingestion frameworks for batch, streaming, API, database, and file-based integrations.\nImplement Delta Lake, Structured Streaming, and medallion architecture patterns.\nIntegrate Databricks with enterprise data platforms such as Snowflake, SAP, Oracle, SQL Server, Azure Data Factory, Kafka, and cloud storage services.\nAI & Machine Learning Enablement\nEnable ML and Generative AI workloads on Databricks.\nImplement MLflow, model lifecycle management, feature stores, and model serving capabilities.\nSupport AI engineers and data scientists with scalable development environments.\nIntegrate Databricks with LLMs, vector databases, AI gateways, and enterprise AI platforms.\nDevOps, DataOps & MLOps\nEstablish CI/CD pipelines for data and AI workloads.\nImplement automated testing, deployment, monitoring, and rollback mechanisms.\nCreate reusable deployment frameworks and engineering accelerators.\nSupport release management and environment promotion processes.\nSecurity, Governance & Compliance\nImplement enterprise security controls, RBAC, data masking, encryption, and audit logging.\nConfigure and manage Unity Catalog governance policies.\nEnsure compliance with enterprise security, privacy, and regulatory requirements.\nPartner with cybersecurity teams to implement platform hardening and vulnerability remediation.\nMonitoring & Reliability Engineering\nImplement platform observability, monitoring, and operational dashboards.\nConfigure logging, alerting, performance monitoring, and incident management processes.\nOptimize platform performance, cost, scalability, and reliability.\nSupport production operations and resolve platform issues.\nCollaboration & Technical Leadership\nCollaborate with architects, data engineers, AI engineers, security teams, and business stakeholders.\nProvide technical guidance and platform best practices.\nParticipate in architecture reviews and platform roadmap planning.\nMentor junior engineers and contribute to engineering excellence initiatives.\nRequired Qualifications\nBachelor's or master’s degree in computer science, Engineering, IT, or a related field.\n5+ years of experience in Data, Platform, or Cloud Engineering.\n3+ years of hands-on experience with the Databricks Lakehouse Platform.\nStrong expertise in Delta Lake, Unity Catalog, MLflow, Databricks Workflows, Structured Streaming, PySpark, and Spark SQL.\nExperience working with cloud platforms such as Azure, AWS, or GCP.\nProficiency in Python and SQL.\nStrong knowledge of DevOps, CI/CD, Infrastructure as Code (IaC), and platform automation.\nHands-on experience with Terraform, GitHub Actions, Azure DevOps, or equivalent CI/CD tools.\nPreferred Qualifications\nExperience with Generative AI, Agentic AI, and LLM-based applications.\nExperience integrating Databricks with Snowflake and enterprise AI platforms.\nKnowledge of Kubernetes, Docker, APIs, Kafka, and event-driven architectures.\nDatabricks Certified Professional or Associate certifications.\nExperience supporting enterprise-scale Data & AI platforms.\nKey Success Metrics\nPlatform availability and reliability.\nDeployment automation and operational efficiency.\nSecurity and compliance adherence.\nData pipeline performance and scalability.\nAI/ML platform adoption and productivity improvements.\nPlatform cost optimization and governance effectiveness.\nIdeal Candidate Profile\nA hands-on engineer who can build, configure, integrate, automate, secure, and operationalize Databricks as an enterprise Data & AI platform while enabling scalable DataOps, MLOps, and AI solutions across the organization.\nOne-line executive summary: Own and engineer the Databricks platform end-to-end, enabling enterprise-scale Data, AI, ML, and Agentic AI solutions through automation, integration, governance, security, and operational excellence.","description_format":"text","description_chars":5550,"description_truncated":false,"requirements":{"experience_years_min":5,"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":["Water Utilities","Chemical Manufacturing","Sterilization & Infection Control"],"lifecycle":[{"event":"open","at":"2026-09-27T11:46:19Z"}],"liveness":{"score":53,"band":"ok","label":"Likely open","p_open":1,"p_active":0.591,"p_room":0.9,"age_days":10,"expected_fill_days":25,"reasons":["conf:1","stale_co","velocity","win:mid","comp:brand"],"computed_at":"2026-10-01T05:45:00Z"},"pay":null,"html_url":"https://alion.io/job/ecolab-ai-engineer-data-databricks-pltform","json_url":"https://alion.io/job/ecolab-ai-engineer-data-databricks-pltform.json","meta":{"generated_at":"2026-10-01T12:45:30Z","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":4195,"day_limit":5000,"remaining_today":805,"minute_limit":60,"resets_at":"2026-10-02T00:00:00Z"}}}