Job Description :

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.

- Transform With State 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.

Total Experience : 8-13 years