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Machine Learning Engineer - Databricks

3 years

0 Lacs

greater chennai area

Posted:2 days ago| Platform: Linkedin logo

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Skills Required

learning databricks ml training mlflow ai design tracking model spark mllib xgboost optimization drift data analytics documentation engineering pytorch tensorflow monitoring management communication aws azure architecture governance

Work Mode

On-site

Job Type

Full Time

Job Description

Description

Job Title : ML Engineer DatabricksLocation : Chennai / BangaloreCompensation : Open to discussNotice 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
(ref:hirist.tech)

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