Senior MLOps Engineer

Senior MLOps Engineer – AI Agent Versioning & Lifecycle ManagementJob SummaryWe are looking for a Senior MLOps Engineer to design and implement enterprise-grade versioning, deployment, and lifecycle management capabilities for AI agents.The role will focus on treating an AI agent—including its container image, prompt configuration, tool bindings, and policy associations—as a single immutable, versioned unit. You will build automated workflows for agent validation, promotion, rollback, and lifecycle management across development, QA, and production environments.The ideal candidate has strong hands-on experience in MLOps/DevOps, AI/ML systems, containerized workloads, CI/CD, AWS, artifact versioning, and enterprise deployment patterns, with a strong understanding of how to apply these practices to LLM-based applications and AI agents.Project OverviewAbout the ClientOur client is a large global enterprise operating across multiple markets, with a complex technology landscape and a strong focus on digital transformation and innovation. The organization is actively investing in modern cloud, data, and AI capabilities to enable scalable, secure, and highly automated solutions across its business.About the ProjectThe project is focused on building an enterprise-grade AI Agent Platform from the ground up. The platform will provide a standardized foundation for developing, deploying, orchestrating, securing, evaluating, and observing AI agents across multiple teams and business use cases.The initiative covers the full agent lifecycle, bringing together agent orchestration, observability, security, governance, integrations, evaluation, and platform engineering.Engineers joining the project will have the opportunity to influence key architectural and technical decisions and contribute to building a new platform rather than maintaining an existing solution.Key ResponsibilitiesDesign and implement an agentic versioning model that treats container images, prompt configurations, tool bindings, and policy associations as a single immutable versioned artifact.Implement version management for AWS AgentCore Runtime, including automated version creation and endpoint management for default and named endpoints.Design and implement automated agent promotion workflows across Dev, QA, and Production environments.Establish evaluation and quality gates that prevent agent versions from being promoted when they fail predefined quality, security, or evaluation criteria.Design and implement agent rollback mechanisms to restore previously validated versions and verify their continued functionality.Manage AI agent container images in AWS ECR, including ARM64 tagging, lifecycle policies, image signing, and immutability controls.Integrate agent versioning workflows with an Agent Registry, automatically registering new versions and associated metadata.Maintain comprehensive version metadata, including evaluation results, promotion status, container/image information, and deployment history.Design and maintain CI/CD pipelines that enforce automated validation and quality gates for AI systems.Implement mechanisms for agent version comparison and regression detection across releases.Ensure complete traceability between source code, container images, prompts, tools, policies, evaluations, registry records, and deployed agent versions.Collaborate with platform, AI/ML, security, and application engineering teams to establish enterprise standards for AI agent lifecycle management.Required Skills & Experience5+ years of experience in MLOps, DevOps, Platform Engineering, or a related discipline supporting AI/ML systems.Hands-on experience with versioning and lifecycle management of ML models, LLM applications, or AI agents.Strong experience implementing immutable artifact versioning at enterprise scale.Strong hands-on experience with AWS ECR and container image lifecycle management.Experience designing and implementing CI/CD pipelines with automated quality and evaluation gates.Experience with containerized AI/ML workloads and production deployment practices.Strong understanding of artifact traceability, version management, release promotion, and rollback strategies.Experience working with AWS cloud services and infrastructure automation.Experience with Git-based development and CI/CD workflows.Strong understanding of software release management across multiple environments.Nice to HaveHands-on experience with AWS AgentCore Runtime versioning and endpoint management.Experience with AI/agent evaluation platforms such as Langfuse, LangSmith, or PromptLayer.Experience implementing multi-environment promotion patterns for AI agents.Experience with agent version comparison and regression detection.Experience with LLM/agent frameworks such as LangChain or LangGraph.Experience implementing container image signing and software supply-chain security.Experience with GitOps-based deployment and promotion patterns.Experience with AI governance, security, and compliance controls.Technical EnvironmentCloud: AWSContainerization: DockerContainer Registry: Amazon ECRRuntime: AWS AgentCore RuntimeCI/CD: GitHub Actions and/or equivalent CI/CD platformsAI/ML: LLMs, AI Agents, ML/AI evaluationEvaluation: Langfuse, LangSmith, PromptLayer or similarVersion Control: Git/GitHubDeployment: Dev → QA → ProductionPractices: Immutable artifacts, automated quality gates, promotion, rollback, traceability, regression detectionEducationBachelor's degree in computer science, Software Engineering, Information Technology, Engineering, or a related field.Why This Position?Work on a greenfield enterprise AI Agent Platform.Help establish the architecture and engineering standards for AI agent lifecycle management.Work at the intersection of MLOps, DevOps, Cloud, AI, and platform engineering.Build enterprise-grade capabilities for agent versioning, evaluation, promotion, and rollback.Collaborate with cross-functional engineering, AI, security, and platform teams.Contribute to a strategic AI transformation initiative within a large global enterprise.