Design and maintain automated ML model deployment pipelines, CI/CD workflows, cloud infrastructure, and scalable production systems. Manage model versioning and experiment tracking, containerize applications with Docker, orchestrate workloads using Kubernetes, and troubleshoot production issues. Develop Python-based tools and enforce MLOps practices to support reliable, reproducible, and efficient machine learning operations.
MLOps Engineer
Summary
We are seeking a skilled MLOps Engineer to join our IT Services team, where you will play a pivotal role in bridging the gap between data science and production environments. This position is dedicated to streamlining the deployment lifecycle of machine learning models, ensuring they are robust, scalable, and efficient. The ideal candidate will leverage their expertise in automation and performance optimization to drive the successful integration of AI solutions into our business operations, directly contributing to the organization's technological advancement and operational excellence.
Responsibilities
- Design and implement automated pipelines for machine learning model deployment and continuous integration.
- Manage cloud infrastructure and utilize specialized MLOps tools to maintain system reliability.
- Oversee model versioning and experiment tracking to ensure reproducibility and auditability of all data science projects.
- Containerize applications using Docker and orchestrate workloads with Kubernetes for scalable deployment.
- Diagnose and resolve production issues related to machine learning systems to minimize downtime.
- Develop and maintain Python-based scripts and applications to support ML workflows.
- Establish and enforce CI/CD practices specifically tailored for machine learning models to accelerate delivery cycles.
Requirements
Requirements:
- Experience: 3-6 years of professional experience in relevant technical roles.
- Technical Proficiency: Strong command of Python programming and experience with Docker and Kubernetes.
- Domain Knowledge: Proven expertise in MLOps tools, cloud platforms, and CI/CD pipelines for machine learning.
- Core Skills: Demonstrated ability in automation, performance optimization, model versioning, experiment tracking, and production troubleshooting.
- Deployment: Hands-on experience with the end-to-end deployment of ML models in production environments.
- Compensation: Salary range of ₹15 LPA.
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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.
