Senior MLOps or Real-Time ML Engineer

Senior MLOps / Real-Time ML Engineer Databricks

Experience: 8+ years

Role Overview

We are looking for a Senior MLOps / Real-Time ML Engineer with 8+ years of experience and extensive hands-on experience with Databricks to build, productionize and operate enterprise-scale ML solutions.

The role combines MLOps, ML engineering and real-time/streaming data engineering, with ownership across the ML lifecycle—from model development and feature engineering to deployment, monitoring and optimization.

Key Responsibilities

• Design and productionize scalable ML/MLOps solutions on Databricks, covering model training, deployment, monitoring, retraining and lifecycle management.
• Build real-time ML pipelines using Kafka and Databricks Structured Streaming, including stateful processing, event-time logic and real-time feature generation.
• Develop offline and online features with strong focus on point-in-time correctness, leakage prevention and training-serving parity.
• Productionize models using MLflow, Unity Catalog, Databricks Model Serving and Databricks-native capabilities.
• Build and automate CI/CD pipelines for ML models and data/ML workflows across development, testing and production.
• Design low-latency inference and real-time decisioning solutions capable of continuously processing live events and generating predictions.
• Implement monitoring for model performance, data/feature drift, pipeline health, latency, reliability and cost.
• Optimize Spark/Databricks workloads for scalability, performance and cost.
• Partner with Data Science, Data Engineering, Platform and Product teams to define and implement scalable ML architecture.
• Build reusable MLOps and real-time ML frameworks that can be applied across multiple use cases.

Required Skills & Experience

• 8+ years in MLOps, ML Engineering, Data Science, Data Engineering or Software Engineering, with significant production ML experience.
• Extensive hands-on Databricks experience, including MLflow, Unity Catalog, Delta Lake and Databricks Workflows/Lakeflow Jobs.
• Strong Python, PySpark and SQL skills.
• Hands-on experience with Kafka and Spark Structured Streaming, including stateful/event-time processing.
• Experience building real-time ML inference and decisioning pipelines.
• Strong understanding of feature engineering, offline/online features, training-serving parity and data leakage prevention.
• Experience with ML frameworks such as XGBoost, LightGBM or scikit-learn.
• Strong understanding of classification, regression, time-series/event-based and/or time-to-event prediction.
• Experience with model deployment, monitoring, model lifecycle management and CI/CD.
• Experience with cloud platforms such as Azure, AWS or GCP; Azure Databricks experience is desirable.
• Ability to make and communicate architecture decisions across ML, data and platform layers.

Good to Have

• Databricks Feature Engineering / Feature Store and online feature stores.
• Databricks Model Serving and low-latency inference platforms.
• transformWithState or equivalent stateful streaming frameworks.
• Experience in real-time decisioning, logistics, supply chain, fraud or recommendation systems.
• Survival analysis / time-to-event modeling.
• Terraform, Docker and Kubernetes.
• Experience with GenAI/LLMOps.

What We Are Looking For

A hands-on engineer who can take an ML solution from prototype to production, build the real-time data and feature pipelines around it, deploy and monitor the model, and establish reusable MLOps capabilities for reliable ML at scale.