Job Summary

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.

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 setting 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, and LLM-based applications.
- Experience integrating Databricks with Snowflake and enterprise AI platforms.
- Knowledge of Kubernetes, Docker, APIs, Kafka, and event-driven architectures.
- Databricks Certified Professional or Associate certifications.
- Experience supporting enterprise-scale Data & AI platforms.

Key Success Metrics

- Platform availability and reliability.
- Deployment automation and operational efficiency.
- Security and compliance adherence.
- Data pipeline performance and scalability.
- AI/ML platform adoption and productivity improvements.
- Platform cost optimization and governance effectiveness.

Ideal Candidate Profile A 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. One-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.

📌 AI engineer-Databricks Data Platform (Bengaluru)
🏢 Ecolab Global Services
📍 Bengaluru