Data Scientist - AI - MLOps / LLMOps Engineer – Senior – EY GDS Spain – Hybrid
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.
Responsibilities
* Build robust, scalable, secure and automated ML/LLM infrastructure to support end‑to‑end model lifecycle management across EY global teams.
* Design, implement, and maintain CI/CD pipelines (Azure DevOps, GitHub Actions, GitLab CI, or similar) for ML/LLM workflows.
* Develop and maintain APIs using Flask, FastAPI, or similar frameworks to operationalise ML/LLM services.
* Manage containerised workloads with Docker, integrating them into Kubernetes (AKS preferred) for scalable deployment.
* Implement monitoring, logging, observability and performance tracking for ML/LLM systems.
* Collaborate effectively with cross‑functional and global teams to translate business requirements into scalable ML/LLM architectures.
Qualifications
* 3+ years in Machine Learning Engineering, MLOps, or related fields with hands‑on experience supporting ML and LLM solutions in production.
* Strong experience with Azure Machine Learning (Azure ML) for training pipelines, model registry, deployment, and automation.
* Practical experience with MLflow for experiment tracking and model lifecycle management.
* Experience building and maintaining CI/CD pipelines specifically for ML/LLM workflows.
* Hands‑on experience with RAG architectures, embeddings, retrieval pipelines, and vector stores.
* Proficiency with LLM frameworks such as LangChain, LangGraph, AutoGen, and Semantic Kernel.
* Advanced Python skills following best software‑engineering practices (Git, testing, versioning, modularisation).
* Experience deploying scalable model and LLM inference services using Kubernetes‑native tools (e.g., KServe, Ray Serve, Triton Inference Server).
* Knowledge of AI governance, responsible AI principles, transparency, and accountability.
* Experience with Azure or other cloud environments for AI deployment and pipeline orchestration.
* Hands‑on experience with Docker and Kubernetes (AKS preferred).
* Strong interpersonal and communication skills for cross‑functional collaboration.
* Bachelor’s or Master’s degree in Computer Science, Engineering, AI, or a related technical field.
Benefits
* Empowering career development with tailored training and development programs.
* Flexible hybrid work model supporting work‑life integration.
* Comprehensive well‑being programs, including psychological support sessions and health resources.
* Meaningful volunteering opportunities to create positive community impact.
* Recognition programs honoring individual and team successes.
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