Lead the architecture, deployment, automation, monitoring, and lifecycle management of scalable machine learning systems. Establish model versioning and ML-specific CI/CD pipelines, improve production reliability, and optimize workflows. Provide technical leadership and mentorship while coordinating with data scientists and software engineers to deliver robust machine learning solutions.
MLOps Lead
Summary
We are seeking an experienced MLOps Lead to spearhead the integration, deployment, and maintenance of machine learning systems within our IT Services sector. In this pivotal role, you will bridge the gap between data science and engineering, ensuring that robust, scalable, and efficient machine learning pipelines are delivered to production. Your leadership will be instrumental in optimizing workflow automation, managing model lifecycles, and fostering a collaborative environment that drives innovation and operational excellence in our AI initiatives.
Responsibilities
- Architect and oversee the design of scalable infrastructure to ensure high reliability and performance of machine learning pipelines.
- Lead the implementation of comprehensive model versioning strategies and lifecycle management protocols to streamline development and deployment.
- Drive the automation of end-to-end workflows, reducing manual intervention and accelerating the time-to-market for new models.
- Establish and maintain CI/CD pipelines specifically tailored for machine learning applications to ensure continuous integration and delivery.
- Provide strategic direction and mentorship to the team, fostering a culture of collaboration and technical excellence.
- Monitor and enhance the stability of production environments, proactively addressing potential bottlenecks or failures in ML deployments.
Requirements
Requirements:
- Minimum of 7+ years of professional experience in the IT Services industry, with a significant focus on machine learning operations.
- Proven expertise in workflow automation tools and techniques for complex data engineering tasks.
- Deep understanding of model versioning systems and lifecycle management best practices.
- Strong background in building and maintaining reliable infrastructure and production-grade ML pipelines.
- Demonstrated ability in leading cross-functional teams and collaborating effectively with data scientists and software engineers.
- Extensive hands-on experience with MLOps frameworks, ML model deployment strategies, and CI/CD methodologies for machine learning.
- Competitive salary package of ₹19 LPA commensurate with experience and expertise.
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Delhi, India's capital city, is a place where tradition and progress co-exist. While Old Delhi is known for its rich history and bustling markets, New Delhi is defined by its modern architecture. It's clear the region places a strong emphasis on preserving its cultural heritage while embracing technological advancements, particularly in artificial intelligence, which plays a central role in shaping the city's tech landscape, fueled by investments in research and development.
