Senior MLOps / LLMOps Engineer - EY GDS Spain - Hybrid
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
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.
Experience & 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 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.
* 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.
* Advanced Python skills following best software engineering practices including Git, testing, versioning, and modularization.
* Experience building APIs using Flask, FastAPI, or similar frameworks for operationalizing ML/LLM services.
* Hands‑on experience with Azure (preferred) or other cloud environments for AI deployment and pipeline orchestration.
* Experience using Docker to package ML and LLM workloads and deploying them in Kubernetes (AKS preferred).
* Strong interpersonal skills with the ability to collaborate effectively across cross‑functional and global teams.
* Bachelor’s or Master’s degree in Computer Science, Engineering, AI, or related technical field.
* Knowledge of AI governance, responsible AI principles, transparency, and accountability.
* Experience deploying scalable model and LLM inference services using Kubernetes‑native tools (e.G., KServe, Ray Serve, Triton Inference Server).
* Experience implementing monitoring, logging, observability and performance tracking for ML/LLM systems.
* Ability to translate complex operational and business requirements into scalable ML/LLM architectures.
Benefits
* Empowering Career Development: tailored training and development programs designed to elevate your skills and propel your career forward.
* Flexible Work‑Life Integration: hybrid work model allowing you to blend professional responsibilities with personal passions.
* Comprehensive Well‑Being Programs: extensive wellness initiatives, including psychological support sessions and health resources.
* Meaningful Volunteering Opportunities: engaging volunteering programs that help you give back to the community.
* Recognized Performance and Rewards: programs that honor both individual and team successes to make you feel valued.
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