Service Delivery Center, AI & Data, AI Developer - Manager

Location: San Antonio Other locations: Anywhere in Region

Salary: Competitive Date: Oct 22, 2026

Job description Requisition ID: 1746400

Location: San Antonio, Alpharetta and Jacksonville At EY, we’re all in to shape your future with confidence. 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. The O pportunity Leads the delivery of solution or infrastructure development services for large or complex AI/ML initiatives, applying strong technical capability and hands-on engineering experience. Takes accountability for the design, development, delivery, and maintenance of AI-enabled solutions or infrastructure, while ensuring compliance with and contribution to relevant engineering standards. Understands business and user

requirements and translates them into design specifications that are effective from both business and technical perspectives. Owns the implementation and integration of AI/ML capabilities into broader enterprise solutions, with a focus on reliability, scalability, user impact, and successful project delivery. Your

key responsibilities Manage design, development, testing, deployment, and support for production-grade AI/ML, generative AI, and intelligent automation solutions. Manage complex technical problems through coding, debugging, testing, troubleshooting, and structured design remediation. Manage build and integration of LLM, RAG, and agentic solution components into enterprise applications and platforms. Contribute to system design across service boundaries, orchestration layers, data flows, security controls, and external integrations. Lead workstreams or project delivery

responsibilities through planning, coordination, execution oversight, issue management, and stakeholder communication. Drive engineering quality through strong coding standards, CI/CD practices, automated testing, observability, and documentation. Partner with Development, Engineering, Product, Data, Architecture, and engagement leadership teams to deliver high-value AI capabilities. Improve performance, resilience, maintainability, and cost efficiency of deployed AI systems. Participate in architecture and design reviews, providing thoughtful trade-off analysis and implementation guidance. Use modern AI-assisted software engineering tools such as Claude Code, Codex, or equivalent agentic coding platforms as part of delivery leadership and engineering execution. AI and Engineering

Skills: Gen AI Foundational: Ability to understand complex technical business challenges across banking, capital markets, insurance, and asset management and translate them into LLM-powered solutions that deliver measurable business value Practical experience leading and managing multi-disciplinary teams through the full AI product lifecycle —

requirements, architecture, build, evaluation, and production handoff Demonstrated experience managing and mentoring teams of AI engineers and data scientists through the execution of specific business use cases, ensuring technical quality and delivery consistency across engagements Advanced hands-on software engineering proficiency in Python, with the credibility to guide implementation decisions as well as architecture across delivery teams Demonstrated experience architecting and delivering production-grade LLM applications including retrieval-augmented systems, agentic orchestration layers, and structured output pipelines at enterprise scale (e.g. LlamaIndex, LangChain, Azure OpenAI, AWS Bedrock) Strong knowledge of embedding models, vector search, semantic retrieval, and NLP similarity systems used in enterprise RAG and knowledge AI architectures (e.g. OpenAI Embeddings, Cohere Embed, Azure AI Search, FAISS etc.) Agentic and LLM Ops: Deep expertise in LLM Ops practices including model lifecycle management, versioning, CI/CD for AI systems, deployment governance, and continuous improvement loops in production environments (e.g. MLflow, Azure ML, GitHub Actions, Kubeflow etc.) Execute on agentic system architecture including multi-agent orchestration, tool use patterns, memory design, and human-in-the-loop workflows for high-stakes production environments (e.g. LangGraph, AutoGen, Semantic Kernel, CrewAI, NVIDIA NIM etc.) Experience governing agent behavior in production environments including audit trail design, cost and latency controls, and reliability management across complex multi-agent pipelines Demonstrated exploration of new LLM techniques and emerging agentic patterns, with the ability to assess their applicability to client challenges and translate them into practical delivery approaches Experience defining and governing LLM evaluation frameworks across teams and engagements, ensuring consistent measurement of output quality, safety, and alignment with business

requirements (e.g. RAGAS, DeepEval, Arize, Weights & Biases etc.) Ability to drive performance, resilience, maintainability, and cost efficiency improvements in deployed LLM and agentic systems, including post-deployment optimization and operational tuning Software Engineering: Knowledge of MLOps practices for continuous integration and continuous deployment of AI systems in cloud environments, including containerization and orchestration for scalable and secure LLM deployment (Azure DevOps, GitHub Actions, Kubeflow, MLFlow etc.) Experience governing API design standards for LLM and agentic systems including contract design, versioning, error handling, retry semantics, and decoupling of AI service consumers from internal model and workflow topology Strong system design capability across service boundaries, asynchronous workflows, data contracts, cloud-native patterns, and secure deployment models for AI-enabled applications Proficiency in containerization and orchestration for deploying and managing scalable LLM applications in production cloud environments (e.g. Docker, Kubernetes, Azure Co