AI engineer-Databricks Data Platform

(CHL) Ecolab; India; Vor Ort; INR 2.600.000 - 4.800.000; Vollzeit

Zusammenfassung

Ecolab in India is seeking a Databricks Platform Engineer to design, build, configure, and operationalize enterprise Data & AI solutions on the Databricks platform. This role focuses on scalable workflows, cloud-native delivery, and real-world GenAI integration.

The ideal candidate will have 5+ years in data/platform engineering, with deep expertise in Databricks, Delta Lake, Unity Catalog, MLflow, PySpark, Spark SQL, and cloud platforms (Azure/AWS/GCP).

Qualifikationen

• Bachelor's or master's degree in CS, Engineering, IT, or a related field.
• 5+ years of experience in Data, Platform, or Cloud Engineering.
• 3+ years with the Databricks Lakehouse Platform.
• Strong experience with Delta Lake, Unity Catalog, MLflow, and Spark SQL.
• Proficient in Python and SQL; solid DevOps and IaC skills.

Aufgaben

• Design, build, configure, and maintain Databricks workspaces across development, testing, and production environments.
• Implement platform standards, reusable frameworks, templates, and best practices.
• Manage Unity Catalog, clusters, SQL Warehouses, compute policies, workspace configurations, and access controls.
• Automate platform deployment and configuration using IaC (Terraform) and CI/CD pipelines.
• Build and integrate scalable data pipelines using Databricks Lakehouse architecture.
• Enable ML/AI workloads with MLflow, feature stores, and model serving capabilities.
• Establish CI/CD pipelines for data and AI workloads, with testing, monitoring, and rollback mechanisms.

Kenntnisse

Databricks Lakehouse

Delta Lake

Unity Catalog

MLflow

PySpark

Spark SQL

Python

SQL

Cloud platforms

DevOps

Ausbildung

Bachelor's or master's in CS/Engineering/IT

Tools

Terraform

GitHub Actions

Azure DevOps

Jobbeschreibung

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.

Education/Work Experience: Degree and 2-4 years experience.

Independence Level/Reports to: Normally reports to AI Engineering Manager.

Additional Job 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.

Education/Work Experience: Degree and 2-4 years experience.

Independence Level/Reports to: Normally reports to AI Engineering Manager.

Job Description: Databricks Platform Engineer

Position Summary

We 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.

The ideal candidate will possess deep expertise in Databricks, cloud platforms, data engineering, MLOps, platform automation, and enterprise integration patterns.

Key Responsibilities

Platform Engineering & Administration

• Design, build, configure, and maintain Databricks workspaces across development, testing, and production environments.
• Implement platform standards, reusable frameworks, templates, and best practices.
• Manage Unity Catalog, clusters, SQL Warehouses, compute policies, workspace configurations, and access controls.
• Automate platform deployment and configuration using Infrastructure as Code (Terraform, CI/CD pipelines).

Data Engineering & Integration

• Build and integrate scalable data pipelines using Databricks Lakehouse architecture.
• Design ingestion frameworks for batch, streaming, API, database, and file-based integrations.
• Implement Delta Lake, Structured Streaming, and medallion architecture patterns.
• Integrate Databricks with enterprise data platforms such as Snowflake, SAP, Oracle, SQL Server, Azure Data Factory, Kafka, and cloud storage services.

AI & Machine Learning Enablement

• Enable ML and Generative AI workloads on Databricks.
• Implement MLflow, model lifecycle management, feature stores, and model serving capabilities.
• Support AI engineers and data scientists with scalable development environments.
• Integrate Databricks with LLMs, vector databases, AI gateways, and enterprise AI platforms.

DevOps, DataOps & MLOps

• Establish CI/CD pipelines for data and AI workloads.
• Implement automated testing, deployment, monitoring, and rollback mechanisms.
• Create reusable deployment frameworks and engineering accelerators.
• Support release management and environment promotion processes.

Security, Governance & Compliance

• Implement enterprise security controls, RBAC, data masking, encryption, and audit logging.
• Configure and manage Unity Catalog governance policies.
• Ensure compliance with enterprise security, privacy, and regulatory requirements.
• Partner with cybersecurity teams to implement platform hardening and vulnerability remediation.

Monitoring & Reliability Engineering

• Implement platform observability, monitoring, and operational dashboards.
• Configure logging, alerting, performance monitoring, and incident management processes.
• Optimize platform performance, cost, scalability, and reliability.
• Support production operations and resolve platform issues.

Collaboration & Technical Leadership

• Collaborate with architects, data engineers, AI engineers, security teams, and business stakeholders.
• Provide technical guidance and platform best practices.
• Participate in architecture reviews and platform roadmap planning.
• Mentor junior engineers and contribute to engineering excellence initiatives.

Required Qualifications

• Bachelor's or master's degree in computer science, Engineering, IT, or a related field.
• 5+ years of experience in Data, Platform, or Cloud Engineering.
• 3+ years of hands-on experience with the Databricks Lakehouse Platform.
• Strong expertise in Delta Lake, Unity Catalog, MLflow, Databricks Workflows, Structured Streaming, PySpark, and Spark SQL.
• Experience working with cloud platforms such as Azure, AWS, or GCP.
• Proficiency in Python and SQL.
• Strong knowledge of DevOps, CI/CD, Infrastructure as Code (IaC), and platform automation.
• Hands-on experience with Terraform, GitHub Actions, Azure DevOps, or equivalent CI/CD tools.

Preferred Qualifications

• Experience with Generative AI, Agentic AI