Engineer/Senior Engineer - DataOps & MLOps

Job Description :

• Design, build, and maintain balanced Data, ML, and AI engineering pipelines to automate data provisioning, model deployment, and enterprise workflow execution.

• Implement and support robust LLMOps and MLOps practices across data, machine learning, and Generative AI systems to automate model evaluation, monitoring, and CI/CD workflows.

• Manage, optimize, and scale Databricks workspace configurations, clusters, and jobs for enterprise data processing and AI/ML workloads.

• Collaborate cross-functionally with data engineers, ML engineers, software engineers, and product leads to design, deploy, and scale data pipelines, feature stores, and ML serving systems into production.

• Implement proactive incident response, event instrumentation, and self-healing mechanisms to detect and remediate system anomalies or data/model quality issues.

• Provide day-to-day operational support, infrastructure upgrades, capacity planning, and cloud resource optimization for data platforms and ML infrastructure.

• Work closely with IT DevOps, SRE, and security teams to enforce governance, data lineage, compliance, and enterprise CI/CD deployment standards.

• Promote engineering best practices, conduct code reviews, participate in on-call rotation support, and contribute to knowledge sharing across teams.

• Develop hands-on Proofs of Concept (POCs) for modern data platforms, feature stores, and real-time streaming tools in collaboration with product and analytics teams.

• Maintain agility towards evolving technology stacks across DataOps and MLOps platforms to continually modernize infrastructure.

Experience Range : 2 - 5 years

Educational Qualifications :

• Bachelor's Degree in Computer Science

• Bachelor's Degree in Software Engineering

• Bachelor's Degree in related technical discipline

Job Responsibilities :

• Design, build, and maintain balanced Data, ML, and AI engineering pipelines to automate data provisioning, model deployment, and enterprise workflow execution.

• Implement and support robust LLMOps and MLOps practices across data, machine learning, and Generative AI systems to automate model evaluation, monitoring, and CI/CD workflows.

• Manage, optimize, and scale Databricks workspace configurations, clusters, and jobs for enterprise data processing and AI/ML workloads.

• Collaborate cross-functionally with data engineers, ML engineers, software engineers, and product leads to design, deploy, and scale data pipelines, feature stores, and ML serving systems into production.