The posting
- 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. Additional Job Description 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, 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.
