Senior Machine Learning Engineer

Job Title: MLOps Engineer (Databricks)

Experience: 4–6 Years

Work Mode: Remote

Budget: 1 LPM

Job Overview

we are seeking an experienced MLOps Engineer with strong hands-on expertise in the Databricks ecosystem to join our Data & AI Engineering team. The selected candidate will be responsible for building, deploying, operationalizing, and monitoring Machine Learning models in production environments while working closely with cross-functional teams such as Data Science, Data Engineering, and DevOps.

This role requires deep technical competence in ML lifecycle management, model observability, CI/CD, cloud-native deployment, and modern data tooling.

Key Responsibilities

• Manage Databricks workspaces, jobs, workflows, Delta Lake, Unity Catalog, and MLflow.
• Implement and optimize end-to-end MLOps workflows, including model training, deployment, monitoring, and retraining.
• Develop scalable ML pipelines using Python, PySpark, SQL, and cloud-native services.
• Deploy ML models across AWS (preferred: SageMaker) with Docker/Kubernetes-based orchestration.
• Build and manage CI/CD pipelines using Jenkins, GitHub Actions, or GitLab CI.
• Design infrastructure using Terraform for reproducibility and scalability.
• Configure monitoring for model performance, drift, and data quality using Databricks Lakehouse Monitoring.
• Utilize Databricks Feature Store and hyperparameter tuning frameworks (Optuna, Ray Tune, etc.).
• Document ML processes and collaborate with Data Scientists, ML Engineers, and DevOps teams.

Required Skill Set (Must-Have)

• Databricks (Core Expertise)
• MLflow for experiment tracking & model lifecycle management
• Python (pandas, scikit-learn, PyTorch/TensorFlow)
• PySpark
• End-to-End MLOps lifecycle experience
• Cloud Platform: AWS (SageMaker experience preferred)
• Containerization & Orchestration: Docker / Kubernetes
• CI/CD: Jenkins, GitHub Actions, GitLab CI

Preferred Skills (Good to Have)

• Terraform or similar Infrastructure as Code tools
• Distributed training frameworks (Optuna, Ray Tune, Horovod)
• Model explainability tools (SHAP, LIME)
• Knowledge of multi-cloud environments (Azure / GCP)

Eligibility Criteria

• Minimum 4–6 years of relevant industry experience
• Hands-on experience with production-grade ML deployments
• Strong problem-solving and analytical skills
• Ability to work independently in a remote setup

How to Apply

Send your resume to: [Confidential Information]