We are seeking a skilled and passionate ML Engineer with 7+ years of experience to join our team. The ideal candidate will be instrumental in developing, deploying, and maintaining machine learning models, with a strong focus on MLOps practices.

This role requires hands-on experience with Azure cloud services, Databricks, and MLflow to build robust and scalable ML solutions.

Location : Bangalore (Current or open to relocate).

Workplace : Hybrid (3 days WFO).

Responsibilities :

- Design, develop, and implement machine learning models and algorithms to solve complex business problems.

- Collaborate with data scientists to transition models from research and development to production-ready systems.

- Build and maintain scalable data pipelines for ML model training and inference using Databricks.

- Implement and manage the ML model lifecycle using MLflow for experiment tracking, model versioning, and model registry.

- Deploy and manage ML models in production environments on Azure, leveraging services like Azure Machine Learning, Azure Kubernetes Service (AKS), or Azure Functions.

- Support MLOps workloads by automating model training, evaluation, deployment, and monitoring processes.

- Ensure the reliability, performance, and scalability of ML systems in production.

- Monitor model performance, detect drift, and implement retraining strategies.

- Collaborate with DevOps and Data Engineering teams to integrate ML solutions into existing infrastructure and CI/CD pipelines.

- Document model architecture, data flows, and operational procedures.

Qualifications :

Education : Bachelors or Masters Degree in Computer Science, Engineering, Statistics, or a related quantitative field.

Skills :

- Strong proficiency in Python programming for data manipulation, machine learning, and scripting.

- Hands-on experience with machine learning frameworks such as Scikit-learn, TensorFlow, PyTorch, or Keras.

- Demonstrated experience with MLflow for experiment tracking, model management, and model deployment.

- Proven experience working with Microsoft Azure cloud services, specifically Azure Machine Learning, Azure Databricks, and related compute/storage services.

- Solid experience with Databricks for data processing, ETL, and ML model development.

- Understanding of MLOps principles and practices, including CI/CD for ML, model versioning, monitoring, and retraining.

- Experience with containerization technologies (Docker) and orchestration (Kubernetes, especially AKS) for deploying ML models.

- Familiarity with data warehousing concepts and SQL.

- Ability to work with large datasets and distributed computing frameworks.

- Strong problem-solving skills and attention to detail.

- Excellent communication and collaboration skills.

Nice-to-Have Skills :

- Experience with other cloud platforms (AWS, GCP).

- Knowledge of big data technologies like Apache Spark.

- Experience with Azure DevOps for CI/CD pipelines.

- Familiarity with real-time inference patterns and streaming data.

- Understanding of responsible AI principles (fairness, explainability, privacy).

Certifications :

- Microsoft Certified : Azure AI Engineer Associate.

- Databricks Certified Machine Learning Associate (or higher).