We are looking for a Machine Learning Ops Engineer for our client. In this role, you will be responsible for designing, implementing, and scaling end-to-end machine learning and AI infrastructure on Databricks. You will contribute to building a unified ML platform that supports multiple business domains, ensuring robust, automated, and scalable ML workflows across the entire lifecycle — from data ingestion and feature engineering to deployment and real-time model serving. You will collaborate closely with data science, platform, and software engineering teams to ensure secure, compliant, and efficient ML operations.

What You’ll Be Doing

• Architect, deploy, and maintain ML pipelines for training, evaluation, deployment, and continuous monitoring using Databricks, MLflow, and related tools.

• Build and manage CI/CD workflows for ML model versioning, testing, and controlled releases.

• Design and operationalize feature pipelines using Databricks Feature Store or similar technologies.

• Implement observability and drift detection for both model and data performance using tools such as Evidently AI, Prometheus, and Grafana.

• Collaborate with platform teams to optimize GPU/CPU clusters, Delta Live Tables, and Unity Catalog for scalability, reproducibility, and compliance.

• Automate infrastructure provisioning using Terraform and Databricks APIs.

• Champion best practices in MLOps, AIOps, and ML governance, ensuring secure and efficient model lifecycle management.

• Partner with data scientists to transition research models into production with minimal friction.

• Evaluate and implement real-time model serving architectures (batch, streaming, or online inference).
  

What You’ll Bring

• 4+ years of experience in MLOps, ML Platform Engineering, or similar production-focused ML roles.

• Strong background in containerization and orchestration (Docker, Kubernetes).

• Proven experience building ML pipelines on Databricks, including MLflow, Delta Lake, Unity Catalog, and Feature Store.

• Proficiency in Python, shell scripting, and Git-based development workflows.

• Experience with monitoring and observability tools such as Prometheus and Grafana.

• Familiarity with streaming data technologies (Kafka, Spark Structured Streaming).

• Experience with IaC (Terraform) and workflow orchestration tools (Airflow, Prefect).

• Excellent problem-solving, communication, and cross-functional collaboration skills.
  

Nice to Haves

• Experience designing multi-model architectures and scalable model serving systems.

• Exposure to LLMOps and generative AI deployment patterns on Databricks or similar platforms.

• Knowledge of AIOps tools for intelligent alerting, anomaly detection, and system optimization.

• Relevant certifications such as Databricks Certified Machine Learning Professional, Azure AI Engineer Associate, or Azure DevOps Engineer Expert.
  

Why Join Them?

💸 Competitive salary package.

💻 Career coaching and mentorship to support your professional growth.

🌈 Diverse and multicultural teams that promote inclusivity.

🦄 Outstanding working environment.

🚀 Continuous training and development opportunities.