Role: Senior Associate MLOps / LLMOps Engineer

Tower: AI Platform Engineering & MLOps (AI Managed Services)

Experience: 510 years

Educational Qualification: Bachelors degree in Computer Science, Engineering, or related field (Masters or relevant cloud/DevOps certifications preferred)

About the Role :

As a Senior Associate MLOps / LLMOps Engineer, you will design, build, and operate cloud-native AI and ML delivery pipelines that enable reliable, secure, and governed promotion of models and AI services from development to production. You will partner with AI engineers, data scientists, and operations teams to ensure models, prompts, and AI services are versioned, monitored, and deployed with confidence in an enterprise AWS environment.

Key Responsibilities :

- Build and maintain AWS-based infrastructure supporting ML, LLM, and AI platforms using infrastructure-as-code principles.

- Implement MLOps and LLMOps patterns to support model training, packaging, deployment, and lifecycle management.

- Design and maintain GitHub-based CI/CD pipelines for ML models, AI services, and infrastructure changes.

- Manage versioning of models, prompts, configurations, and artifacts across environments.

- Securely manage secrets, credentials, and sensitive configuration using AWS-native and approved enterprise tooling.

- Deploy AI and ML services using containerized and cloud-native patterns.

- Implement monitoring and alerting for AI services, model endpoints, and pipelines.

- Partner with operations teams to support production readiness, stability, and release governance.

Tech Stack & Requirements :

- Hands-on experience with AWS cloud services and infrastructure.

- Strong understanding of MLOps and LLMOps concepts and lifecycle management.

- Experience building CI/CD pipelines using GitHub.

- Solid DevOps fundamentals, including automation and environment management.

- Experience managing secrets, secure configurations, and model/artifact versioning.

- Experience deploying services and supporting controlled production releases.

Preferred Skills :

- Experience with containerized deployments and orchestration platforms.

- Familiarity with enterprise monitoring and logging tools.

- Exposure to governance, risk, and compliance requirements for AI systems.

- AWS certifications (Developer, DevOps Engineer, Solutions Architect).

- Experience supporting regulated or large-scale enterprise environments.