Experteer Overview In this role you will design, automate, deploy and operate ML and LLM systems for EY’s global teams. You will build robust, scalable ML/LLM infrastructure supporting end-to-end model lifecycles, with a focus on governance, security and automation. You will work across cross-functional teams to implement cutting-edge pipelines (RAG, vector stores) and modern orchestration. This is a hands-on opportunity to shape production ML/LLM platforms in a hybrid EY GDS environment.Compensaciones / Beneficios
- Design, automate and operate ML/LLM systems in production
- Build and maintain scalable ML/LLM infrastructure for end-to-end lifecycle management
- Develop and optimize CI/CD pipelines for ML/LLM workflows
- Leverage Azure ML for training, deployment and model registry
- Implement RAG architectures and vector stores for retrieval-augmented applications
- Utilize frameworks like LangChain, LangGraph, AutoGen, Semantic Kernel for orchestration
- Develop APIs (Flask/FastAPI) to operationalize ML/LLM services
- Containerize workloads with Docker and deploy on Kubernetes (AKS)
- Collaborate with cross-functional and global teams to drive governance and reliability
- Monitor, log and ensure performance of ML/LLM systemsResponsabilidades
- 3+ years in ML Engineering, MLOps or related fields with production ML/LLM experience
- Strong experience with Azure Machine Learning (training pipelines, model registry, deployment, automation)
- Hands-on with MLflow for experiment tracking and lifecycle management
- Experience building CI/CD pipelines for ML/LLM workflows (Azure DevOps, GitHub Actions, GitLab CI)
- Experience with RAG architectures, embeddings, retrieval pipelines and vector stores
- Proficiency with LangChain, LangGraph, AutoGen, Semantic Kernel or equivalent tools
- Advanced Python skills with version control and testing
- API development experience (Flask, FastAPI)
- Azure or other cloud experience for AI deployment and orchestration
- Docker and Kubernetes (AKS preferred)
- Strong collaboration and communication skills
- Bachelor’s or Master’s in CS/Engineering/AI or related fieldRequisitos principales
- Hybrid work model
- Flexible work-life integration
- Well-being programs including psychological support
- Volunteering opportunities
- Recognition and rewards programs
- Career development and training opportunities