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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.
- Robust 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

📌 eClerx - Senior MLOps Engineer (India)
🏢 eClerx
📍 India