Senior / Lead ML Engineer (Databricks MLOps)

Location: Hyderabad (Hybrid)

Experience: 8 Years+

Company: Anblicks

Role Overview

Anblicks is hiring a Senior Level MLOps Engineer with Expertise in Databricks to drive the design, implementation, and scaling of enterprise-grade ML platforms. This role is ideal for a hands-on leader who can own end-to-end MLOps strategy and execution, while mentoring teams and delivering production-ready ML systems on Databricks.

You will play a key role in building scalable, reliable, and automated ML pipelines, enabling faster experimentation, deployment, and monitoring of models across business use cases.

Key Responsibilities

MLOps Leadership & Architecture

• Lead the design and implementation of scalable MLOps frameworks on Databricks
• Define and enforce best practices, standards, and governance for ML lifecycle management
• Architect end-to-end ML pipelines covering data ingestion, feature engineering, training, deployment, and monitoring
• Drive platform standardization for model development and operationalization
  

• Build and optimize solutions using Databricks ecosystem: MLflow, Unity Catalog, Workflows, Delta Lake, Mosaic AI
• Develop high-performance Spark-based pipelines for large-scale data processing
• Enable model versioning, experiment tracking, and reproducibility
• Ensure scalability, security, and performance of ML platforms
  

• Implement CI/CD pipelines for ML workflows using GitHub Actions, Jenkins, Terraform, or similar tools
• Automate model deployment, monitoring, and retraining pipelines
• Work with containerization and orchestration tools like Docker and Kubernetes
• Establish observability and monitoring frameworks for ML systems
  

• Support development of LLM-based and GenAI solutions
• Build RAG pipelines and integrate LLMs into enterprise workflows
• Evaluate and onboard tools like OpenAI, Bedrock, Vertex AI, LangGraph
  

• Collaborate with Data Scientists, Data Engineers, and Product Teams to productionize ML use cases
• Translate business requirements into scalable ML solutions
• Provide technical leadership, mentorship, and code reviews
• Drive continuous improvement and innovation in ML practices
  

• 7–12 years of experience in ML Engineering / MLOps / Data Engineering
• Strong hands-on experience with Databricks (must-have)
• Expertise in MLflow, Unity Catalog, Workflows, Delta Lake
• Strong programming skills in Python (mandatory)
• Experience with Apache Spark and distributed data processing
• Solid understanding of ML lifecycle and model deployment strategies
• Experience with CI/CD and DevOps practices
• Hands-on experience with Airflow, Kubeflow, or similar orchestration tools
• Experience working with AWS / Azure / GCP cloud platforms
  

• Databricks Certification (Associate / Professional / Architect)
• Experience with GenAI / LLM ecosystems
• Familiarity with vector databases (Pinecone, ChromaDB, etc.)
• Experience with Docker, Kubernetes, and infrastructure as code (Terraform)
• Exposure to model monitoring and observability tools
  

• Strong ownership mindset with the ability to lead from the front
• Excellent problem-solving and analytical thinking
• Ability to manage multiple priorities in a fast-paced environment
• Strong communication skills with both technical and business stakeholders
• Mentorship experience and team collaboration skills
  

• Work on cutting-edge Databricks and AI/ML platforms
• Opportunity to lead enterprise-scale ML transformations
• Collaborative and innovation-driven engineering culture
• Exposure to modern data + AI ecosystem (Snowflake, Databricks, GenAI)