Service Delivery Center, AI & Data, Project-Based Long Duration - Manager

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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.)

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