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 general 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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