MLOps / LLMOps Engineer - Senior - EY GDS Spain - Hybrid

MLOps/LLMOps Engineer – Senior – EY GDS Spain – Hybrid 
            

The opportunity  

We are seeking a highly skilled Senior MLOps / LLMOps Engineer with 3+ years of experience designing, automating, deploying and operating Machine Learning and Large Language Model (LLM) systems in production environments. 

The ideal candidate will have strong experience with Azure ML, MLflow, CI/CD, 
containerized deployments, orchestration platforms, and modern LLM 
pipelines (RAG, vector DBs, LangChain). 

You will be responsible for building robust, scalable, secure and automated  ML/LLM infrastructure to support end-to-end model lifecycle management across EY global teams. 

To qualify for the role, you must have 

Experience: 

• 3+ years in Machine Learning Engineering, MLOps, or related fields, with hands- on experience supporting ML and LLM solutions in production. 

MLOps & Platform Engineering: 

• Strong experience with Azure Machine Learning (Azure ML) for training 
pipelines, model registry, deployment, managed compute and automation. 

• Practical experience with MLflow for experiment tracking, reproducibility and  model lifecycle management. 

• Experience building and maintaining CI/CD pipelines (Azure DevOps, GitHub  Actions, GitLab CI or similar) specifically for ML/LLM workflows. 

LLMOps & Frameworks: 

• Hands-on experience with RAG architectures, embeddings, retrieval pipelines  and vector stores. 

• Proficiency with frameworks such as LangChain, LangGraph, AutoGen, 
Semantic Kernel or equivalent tools used in LLM application orchestration. 
Programming & Engineering: 

• Advanced Python skills following best software engineering practices including  Git, testing, versioning, and modularization. 

API Development: 

• Experience building APIs using Flask, FastAPI, or similar frameworks for 
operationalizing ML/LLM services. 

Cloud Platforms: 

• Hands-on experience with Azure (preferred), or other cloud environments for AI  deployment and pipeline orchestration. 

Containerization & Orchestration: 

• Experience using Docker to package ML and LLM workloads and deploying them in Kubernetes (AKS preferred). 

Collaboration & Communication: 

• Strong interpersonal skills with the ability to collaborate effectively across cross- functional and global teams. 

Education: 

• Bachelor’s or Master’s degree in Computer Science, Engineering, AI, or a related  technical field. 

Ideally, you’ll also have 

Trusted AI Practices: 

• Knowledge of AI governance, responsible AI principles, transparency, and 
accountability. 

Deployment & Scalability: 

• Experience deploying scalable model and LLM inference services using  Kubernetes-native tools (e.g., KServe, Ray Serve, Triton Inference Server). 
Monitoring & Reliability: 

• Experience implementing monitoring, logging, observability and performance 
tracking for ML/LLM systems. 

Analytical & Problem-Solving Skills: 

• Ability to translate complex operational and business requirements into scalable ML/LLM architectures.