Job Description
Experience: 5+ years
Education: B.Tech / B.E. (Computer Science, IT, or related discipline) or equivalent practical experience
Role Overview
We are looking for experienced MLOps / DevOps Engineers to support a large-scale application migration and modernization program. The role involves migrating existing applications and ML workloads to containerized, cloud-native platforms using OpenShift and AWS ROSA, while ensuring scalability, reliability, and operational excellence.
You will work closely with platform, ML, and application teams to redesign, migrate, deploy, and operate workloads in a modern DevOps/MLOps ecosystem.
Key Responsibilities
Lead and execute application and ML workload migrations from legacy or VM-based environments to containerized platforms.
Design, deploy, and manage infrastructure on AWS, with a strong focus on ROSA (Red Hat OpenShift on AWS).
Build and operate CI/CD pipelines for application and ML lifecycle management.
Containerize applications using Docker and deploy/manage them on Kubernetes/OpenShift.
Implement MLOps practices for model training, deployment, versioning, monitoring, and rollback.
Collaborate with development and data science teams to optimize performance, cost, and scalability.
Ensure platform reliability through monitoring, alerting, logging, and automation.
Participate in troubleshooting, root cause analysis, and continuous improvement initiatives.
Required Skills & Experience
5+ years of hands-on DevOps / MLOps experience in production environments.
Strong experience with Docker, Kubernetes, and container orchestration.
Practical experience with OpenShift; AWS ROSA exposure is a strong advantage.
Solid understanding of AWS cloud services, networking, IAM, and security best practices.
Experience with Infrastructure as Code (IaC) using Terraform / Terragrunt.
Strong scripting skills in Python, Bash, Go, or Ruby.
Good working knowledge of Linux systems, container runtimes, and distributed systems.
Experience with monitoring and observability tools such as Prometheus, Grafana, and ELK.
Familiarity with CI/CD tools and GitOps-based workflows.
Good to Have
Exposure to ML frameworks, GPU-based workloads, and large-scale ML operations.
Experience operating or scaling systems like Elasticsearch, Kafka, Spark, or HBase.
Understanding of model lifecycle management, feature stores, and ML pipelines.
Experience in cloud migration or modernization programs.
Knowledge of security, compliance, and platform hardening in Kubernetes/OpenShift environments.
Soft Skills
Strong analytical and problem-solving abilities.
Clear communication and documentation skills.
Ability to work in cross-functional teams and fast-paced environments.
