MLOPS Lead-

Senior ML Engineer

As a Senior MLOps Engineer, the role requires hands-on experience in building and managing ML

Systems in Production with excellent MLOPS Skill sets. The position would be responsible for

building machine learning pipelines and ML Application services and providing machine learning

engineering support for Production teams .

This role operates at the crossroads of Machine Learning, Software/Data Engineering, and DevOps,

leveraging best practices from each domain to optimize the deployment and management of

machine learning models in production environments.

The Position would also be expected to be able to enhance the Technology stack, focusing on

continuous improvements in the reliability, automation and efficiency of the Platform Solutions.

Roles and responsibilities

● Manage ML Platform infrastructure to automate and accelerate model development and

deployment

● Define and build scalable ML pipelines that enables data scientists to build better models

● Build dashboards and other Monitoring tools to optimize performance and Infrastructure

Costs

● Monitor and provide support to infrastructure and in production systems

● Identify possible issues and performance leakages in the system and perform optimization

● Mentoring team members through code review and knowledge sharing

● Lead, mentor, and manage a team of Mlops engineers, fostering a collaborative and high-

performance environment.

● Oversee the team’s project management, ensuring timely and successful delivery of data

solutions.

● Design, develop, and maintain data science models and algorithms along with the DS team

to drive business insights and decision-making onto the ML Platforms

● Implement and manage CI/CD pipelines of Core Solution Packages and Applications

● Containerize applications and workflows using Docker and App Service for consistent

environments and deployment.

● Work closely with various teams, including data science, engineering, and business units, to

align ML Platform Solution with client goals.

● Facilitate communication and collaboration between team members and other departments

to ensure cohesive project execution.

● Provide technical support and insights to cross-functional teams as needed.

● Ensure ML systems comply with security standards and best practices in a cloud

environment.

● Identify and address technical debt in current ML projects, incorporating best MLOps

practices.

Preferred Experience

● 8 + years of hands-on experience at scale in data science / ML engineering

● Excellent hands on skills to write clean and structured SQL, Python and Shell programs

● Good experience with infrastructure, including Cloud Computing, Linux OS, Networks,

Kubenetes, Docker, Infrastructure as Code, RDBMS and NoSQL Databases

● Good understanding of Machine learning concepts and MLops Best practices.

● Quality experience in serving real-time, production-level machine learning models

● Proven experience leading and Managing a team

● Excellent communication skills

● 5+ years hands-on experience with Azure Cloud Technologies such as Azure

Databricks, Azure DevOps, Azure App Service, Docker, Azure Key Vault, and other

managed Azure services.

● Proficient in AWS technologies, including Athena, Glue, ECS, EKS, and VPC, as well as AWS

SageMaker for deploying machine learning models, improving automation, and

implementing essential checks.

● Proficiency in Python for developing automation Scripts, and Pipelines

● Familiarity with Infrastructure as Code (IaC) tools such as Terraform or AWS

CloudFormation for managing cloud infrastructure.

● Basic Familiarity with the Data Science and Machine Learning lifecycle, as well as

frameworks like scikit-learn, PyTorch, and TensorFlow.

Every individual comes with a different set of skills and qualities so even if you don’t tick all the boxes for the role today, we urge you to apply as there might be a suitable/unique role for you tomorrow.

We are an equal-opportunity employer. Our diverse and inclusive culture and values guide us to listen, trust, respect, and encourage people to grow the way they desire.

Note: The designation will be commensurate with expertise and experience. Compensation packages are among the best in the industry.

Additional Benefits: Health insurance (self & family), virtual wellness platform, and knowledge communities.