Role Overview :

As an MLOps / AI Infrastructure Engineer based in Bangalore, you will serve as the backbone of our machine learning initiatives, bridging the gap between data science experimentation and production - grade scalability. You will spend your day designing, deploying, and maintaining robust AI infrastructure that empowers our data science teams to iterate faster and deploy models with high reliability.

Working closely with cross functional teams including Data Scientists, Software Engineers, and Product Managers, you will ensure our ML pipelines are efficient, secure, and cost - optimized. Your work directly impacts the business by reducing time to market for AI features and ensuring our models deliver consistent, high - performance results to our end users.

Key Responsibilities :

- Architect and manage scalable ML infrastructure on AWS to provide a stable environment for model training and inference.

- Build and maintain automated CI/CD pipelines for machine learning, ensuring seamless integration and deployment of models into production environments.

- Orchestrate containerized workloads using Kubernetes and Docker to ensure high availability and efficient resource utilization across our AI clusters.

- Implement Infrastructure as Code (IaC) using Terraform and Ansible to automate provisioning and configuration management, reducing manual overhead and deployment errors.

- Collaborate with data engineering teams to optimize data ingestion and preprocessing workflows, ensuring high - quality data availability for model training.

- Monitor and optimize model performance in production, implementing robust logging and alerting systems to proactively address latency or drift issues.

- Drive the adoption of MLOps best practices across the organization to standardize model lifecycle management and improve overall engineering velocity.

Required Skillset :

- Demonstrated expertise in designing and managing cloud - native AI infrastructure, with a deep understanding of AWS services and cloud computing principles.

- Strong proficiency in container orchestration and management using Kubernetes and Docker, with the ability to troubleshoot complex cluster issues.

- Proven ability to develop and maintain sophisticated CI/CD pipelines, utilizing Python for automation and scripting to streamline ML workflows.

- Hands - on experience with deep learning frameworks such as TensorFlow and PyTorch, and a clear understanding of the end - to - end machine learning lifecycle.

- Exceptional communication skills, with the ability to translate complex technical infrastructure requirements into actionable insights for non - technical stakeholders.

- Strong collaborative mindset, capable of working effectively in a hybrid work environment with distributed teams to drive project milestones.

- A Bachelors or Masters degree in Computer Science, Engineering, or a related quantitative field, reflecting a strong foundation in software engineering principles.

- Ability to thrive in a fast - paced, high - growth environment, demonstrating adaptability when navigating evolving technical requirements and business priorities.