ML ops & LLM Ops Engineer

Job description

**Senior Associate -- MLOps / LLMOps Engineer** **Role:** Senior Associate -- MLOps / LLMOps Engineer **Level:** Senior Associate **Tower:** AI Platform Engineering \& MLOps (AI Managed Services) **Experience:** 5--10 years **Key Skills:** AWS Cloud \& Infrastructure; MLOps \& LLMOps; DevOps \& CI/CD; Model \& Artifact Versioning; Secure Deployments; Observability \& Release Governance **Educational Qualification:** Bachelor's degree in Computer Science, Engineering, or related field (Master's or relevant cloud/DevOps certifications preferred) **Work Location:** Bangalore and Hyderabad (based on your preference) **Job Description** 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. This role is hands-on and execution-focused, emphasizing **automation, reliability, and controlled production releases** for ML and LLM-based systems. **Key Responsibilities** **AWS Cloud \& Infrastructure Engineering** * Build and maintain **AWS-based infrastructure** supporting ML, LLM, and AI platforms. * Use infrastructure-as-code principles to ensure repeatable and auditable environments. * Configure IAM roles, networking, logging, and monitoring aligned to enterprise standards. **MLOps \& LLMOps Enablement** * Implement **MLOps and LLMOps patterns** to support model training, packaging, deployment, and lifecycle management. * Support deployment of traditional ML models as well as LLM-based services and workflows. * Enable reproducibility across environments through standardized pipelines and artifacts. **CI/CD \& DevOps Automation** * Design and maintain **GitHub-based CI/CD pipelines** for ML models, AI services, and infrastructure changes. * Automate build, test, packaging, and deployment workflows. * Enforce quality gates and approvals prior to environment promotion. **Versioning \& Release Management** * Manage **versioning of models, prompts, configurations, and artifacts** across environments. * Support controlled promotion from development to test, staging, and production. * Implement rollback strategies and release validation checks to minimize production risk. **Secrets \& Configuration Management** * Securely manage **secrets, credentials, and sensitive configuration** using AWS-native and approved enterprise tooling. * Enforce least-privilege access and rotation policies. * Ensure separation of configuration across environments. **Deployment \& Environment Management** * Deploy AI and ML services using containerized and cloud-native patterns. * Support blue/green, canary, or phased deployments where applicable. * Ensure deployments are repeatable, traceable, and compliant with change governance. **Monitoring, Logging \& Observability** * Implement **monitoring and alerting** for AI services, model endpoints, and pipelines. * Track service health, deployment status, and runtime performance. * Support operational dashboards and metrics for platform and service visibility. **Production Support \& Controlled Promotion** * Partner with operations teams to support production readiness and stability. * Participate in release readiness reviews and production cutovers. * Ensure promotion to production follows defined governance, approvals, and validation criteria. **Collaboration \& Continuous Improvement** * Collaborate with AI engineers, data scientists, and platform teams to streamline delivery workflows. * Identify opportunities to improve reliability, security, and developer productivity. * Contribute reusable pipeline templates, standards, and documentation. **Required Skills** * 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 and secure configurations**. * Familiarity with **model and artifact versioning** practices. * Experience deploying services and supporting **controlled production releases**. * Strong collaboration and documentation skills. **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.