Description :

Job Title : ML Engineer Databricks

Location : Chennai / Bangalore

Compensation : Open to discuss

Notice Period : Immediate joiners or candidates serving notice with less than 60 days

Job Overview :

We are seeking an experienced ML Engineer with strong hands-on expertise in building, training, and deploying end-to-end machine learning workflows on Databricks. The role involves working closely with enterprise clients to deliver scalable, production-grade ML solutions using MLflow, Mosaic AI, and the Databricks Lakehouse platform.

Key Responsibilities :

- Design, develop, and deploy end-to-end machine learning pipelines on Databricks

- Build, train, evaluate, and optimize ML models at scale for batch and real-time use cases

- Implement experiment tracking, model registry, and version control using MLflow

- Engineer reusable and shareable features using Databricks Feature Store

- Deploy models using Databricks Model Serving for real-time and batch inference

- Leverage AutoML to establish baselines and accelerate model development

- Perform distributed training using Spark MLlib, XGBoost, and Horovod

- Implement hyperparameter optimization workflows using Hyperopt

- Monitor model performance, drift, and data quality using Lakehouse Monitoring

- Collaborate with data engineers, analytics teams, and stakeholders to deliver high-impact solutions

- Create clear technical documentation and effectively communicate with clients

Technical Skills Required :

(68 of the following)

- MLflow (experiment tracking, model registry, model versioning)

- Databricks Feature Store for feature engineering and reuse

- Databricks Model Serving for real-time and batch inference

- Databricks AutoML for rapid baseline model development

- Distributed ML using Spark MLlib, XGBoost, and Horovod

- PyTorch and TensorFlow on Databricks (single-node and distributed training)

- Hyperopt for large-scale hyperparameter tuning

- Lakehouse Monitoring for model drift detection and data quality tracking

Experience & Qualifications :

- 3+ years of experience in Machine Learning Engineering

- At least 1 year of hands-on experience deploying ML models on Databricks

- Proven delivery of 2+ production-grade ML models using MLflow tracking and model serving

- Strong understanding of ML lifecycle management and MLOps best practices

- Excellent communication skills with the ability to interact with enterprise clients and produce high-quality technical documentation

Nice to Have :

- Experience with Mosaic AI or Generative AI workloads on Databricks

- Exposure to cloud platforms (AWS, Azure, or GCP)

- Familiarity with data lakehouse architecture and governance frameworks