MLOps Engineer

Andersen is hiring an MLOps Engineer for a project delivering scalable digital solutions and supporting reliable AI and technology platforms across diverse organizations.

Responsibilities:

• Designing and owning the end-to-end MLOps architecture for production Machine Learning and Generative AI systems.
• Building and maintaining ML training, validation, deployment, serving, monitoring, and retraining pipelines. 
• Building and maintaining CI/CD pipelines for ML and AI workloads with automated testing, approval controls, and rollback mechanisms. 
• Collaborating closely with Platform Engineering to run AI workloads on shared Kubernetes infrastructure. 
• Translating ML infrastructure requirements into technical requirements for platform and architecture teams. 
• Establishing reusable MLOps project templates, shared pipeline components, and engineering standards. 
• Building and operating the production layer for Generative AI and LLM-based applications. 
• Implementing LLM model gateways, routing, prompt management, prompt versioning, caching, and rate limiting.
• Optimizing LLM workloads through model routing, prompt/context optimization, caching, batching, and selection of cost-efficient models.
• Making and documenting architectural and build-vs-configure decisions for new AI capabilities.

Must-haves:

• Strong engineering experience across Software Engineering, DevOps/SRE, and MLOps for 5+ years.
• Production ownership of machine learning systems: you have operated, supported, and been accountable for ML in production.
• Deep hands-on experience with Docker and Kubernetes, including resource management and GPU workloads.
• Strong proficiency with GitLab CI, GitHub Actions, ArgoCD, or equivalent, including pipeline-enforced quality gates.
• Extensive experience with MLflow, Kubeflow, Feast, BentoML, KServe, SageMaker, Vertex AI, Databricks, or comparable platforms.
• Python at production engineering standard (typed, tested, packaged, and reviewed).
• Hands-on LLMOps experience: model gateways, prompt versioning, RAG pipelines,
• vector databases, evaluation harnesses, and guardrail frameworks.
• Generative AI consumption management: token metering, cost attribution across multiple applications or tenants, budget and quota controls, and demonstrable cost optimization of LLM workloads.
• Experience operating agentic AI systems in production.
• Experience establishing an ML platform capability from an early stage.
• Experience with data quality frameworks and workflow orchestration. 
• Level of English – Upper-Intermediate and above.