Service Delivery Center, AI Developer - Manager

About the role

The Opportunity 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.

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.

Requirements

Minimum of 6 years of applied engineering experience, including significant experience in AI/ML engineering roles. Clear communicator able to explain complex AI system behavior and trade-offs to technical and non-technical stakeholders, including risk and compliance. Strong ownership and accountability, taking responsibility for AI systems from design through production and issue resolution. Collaborative and cross-functional, working closely with engineering, product, risk, legal, and audit teams.

Qualifications

• A bachelor's or master’s degree.
• 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.
• 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.
• 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.).
• 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.).
• Experience governing agent behavior in production environments including audit trail design, cost and latency controls, and reliability management across complex multi-agent pipelines.
• 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.
• 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.).
• 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 Container Apps, AWS ECS etc.).
• Collaboration with data engineers, ML engineers, and business stakeholders to align LLM solution design with enterprise data and technology constraints.

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.
• Advanced hands-on software engineering proficiency in Python, with the credibility to guide implementation decisions as well as architecture across delivery teams.
• 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.).
• 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.
• 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.
• 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.).
• 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 Container Apps, AWS ECS etc.).
• Collaboration with data engineers, ML engineers, and business stakeholders to align LLM solution design with enterprise data and technology constraints.

Benefits

Comprehensive compensation and benefits package including medical and dental coverage, pension and 401(k) plans, and a wide range of paid time off options.

Pay

Base salary range for this job in all geographic locations in the US is $76,200 to $174,100. The base salary range for New York City Metro Area, Washington State and California (excluding Sacramento) is $91,400 to $197,900. Individual salaries within those ranges are determined through a wide variety of factors including but not limited to education, experience, knowledge, skills and geography.

Schedule

Our expectation is for most people in external, client serving roles to work together in person 40-60% of the time over the course of an engagement, project or year.

Equal Opportunity Employer

EY is committed to providing reasonable accommodation to qualified individuals with disabilities and encourages applicants with disabilities, including those who are currently military or transitioning out of the military, to apply for employment.