Senior Associate – MLOps / LLMOps Engineer

About the job

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.