Sr. Engineer - Data & MLOps

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.
• 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.

Requirements:

• Strong problem-solving mindset.
• Exceptional cross-functional communication.
• A track record of driving collaborative DevOps/DataOps/MLOps culture.
• Solid understanding of modern DevOps, MLOps, and DataOps methodologies, including CI/CD automation, model governance, and observability tools (e. g., MLflow, Weights and Biases, LangSmith).
• Hands-on experience with core cloud data and ML services on AWS (e. g., SageMaker, Glue, EMR, Athena, S3).
• Strong expertise in Databricks management, optimization, and workspace administration.
• Strong understanding of database fundamentals, replication, relational/NoSQL databases, and vector databases (e. g., Pinecone, FAISS, Milvus, and Weaviate).
• Proven experience building CI/CD automation pipelines for containerized Python, Java, or Scala microservices and ML serving systems.
• Proficient with Git version control and standard branching workflows.
• Practical experience deploying, monitoring, and debugging distributed data pipelines, ETL workflows, and model deployment systems.
• Familiarity with configuration management and provisioning tools (e. g., Terraform, CloudFormation, Ansible).
• Scripting proficiency in one or more languages (Python, Bash, or JavaScript).
• Practical experience with containerization using Docker and exposure to Kubernetes orchestration.
• Solid grasp of the machine learning lifecycle, deep learning frameworks (PyTorch, TensorFlow), NLP/CV concepts, and feature engineering workflows.
• 2 - 5 years of experience across DataOps, MLOps, ML Engineering, or Data Engineering in enterprise cloud environments.