Service Delivery Center, AI Developer - Senior

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• We’ll help you succeed in a globally connected powerhouse of diverse teams and take your career wherever you want it to go.  Join EY and help to build a better working world.
• SDC AI Developer - Senior
• The  O pportunity
• Your key responsibilities
• Develop, test, deploy, and support production-grade AI/ML, generative AI, and intelligent automation solutions.
• Solve complex technical problems across development, integration and production support through coding, debugging, testing, troubleshooting, and structured design remediation.
• Translate user requirements into technical designs, APIs, workflows, and supportable implementation patterns.
• Build and integrate LLM, RAG, and agentic solution components into enterprise solutions, applications and platforms.
• Support project delivery through disciplined execution, estimation, documentation, status’ communication, and risk identification.
• Participate in design reviews, providing thoughtful trade-off analysis and implementation input.
• Experience designing, building, and maintaining production-grade LLM applications, including end-to-end pipelines from data ingestion through model output delivery (e.g. Azure OpenAI, AWS Bedrock, Google Vertex AI etc.).
• Working technical knowledge of embedding models, vector search, and semantic retrieval patterns used to ground LLM outputs in enterprise knowledge sources (e.g. OpenAI Embeddings, Azure AI Search, pgvector etc.).
• Ability to debug, troubleshoot, and remediate production LLM and agentic systems including failure diagnosis across retrieval, orchestration, and generation layers.
• Hands-on software engineering proficiency in Python, with the ability to write clean, modular, production-quality code for LLM pipelines and agentic applications.
• Experience working with structured and unstructured data sets to support LLM application development, including data curation, preparation, and quality validation for model inputs and model responses.
• Working familiarity with RESTful and event-driven API patterns including asynchronous workflows, service boundaries, and integration of enterprise data sources to expose LLM and agentic capabilities.
• Practical understanding of containerization and orchestration concepts for packaging and deploying LLM applications in cloud environments (e.g. Docker, Kubernetes, Azure Container Apps, AWS ECS etc.).
• Understanding of software engineering best practices as applied to ML systems, including modular code design, testing patterns for AI pipelines, and data quality validation.
• Familiarity with Data Monitoring and Data Observability in cloud environments (Open Telemetry, Azure Application Insights etc.).
• Exposure to CI/CD and operationalization practices for AI systems, including model and workflow deployment, versioning, environment promotion, and release support in cloud or containerized environments.
• To qualify for the role you must have
• A bachelor's or master’s degree
• Minimum of 2 years of related work experience applied engineering experience, including meaningful experience in AI/ML engineering roles

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