Machine Learning Engineer

Machine Learning Engineer

Bangalore - 3 days work from office

MAX 18.50 LPA

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: Bachelor’s or Master’s Degree in Computer Science, Engineering, Statistics,

or a related quantitative field.

● Experience: Minimum 3+ years of professional experience as an ML Engineer or in a

similar role.

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)