Principal Generative AI Developer and Lead, SVP

Job Summary The USCC Architecture and AI Engineering group is seeking a seasoned and visionary Senior Generative AI/AI Developer and Lead to guide our team and architect the next generation of intelligent systems. This is a pivotal role in driving our AI strategy, from conceptualization through to production. We are looking for an expert with a proven track record in designing and delivering robust, scalable, and well-governed AI solutions. The ideal candidate will be a technical leader who can mentor a talented team of engineers, collaborate effectively with business stakeholders, and steer our technical direction in the rapidly evolving landscape of Generative and Agentic AI. Key Responsibilities Architect & Design: Lead the design and architecture of complex, end-to-end AI systems, ensuring they are scalable, resilient, maintainable, and secure. Strategize & Collaborate: Partner directly with business leaders and product managers to identify high-impact AI opportunities, define project roadmaps, and translate strategic business needs into a coherent technical vision. Lead & Mentor: Manage, mentor, and develop a team of AI engineers, fostering a culture of innovation, technical excellence, and rapid, continuous learning. Provide technical guidance and oversight on all projects. Govern & Standardize: Establish, implement, and enforce AI governance frameworks and MLOps best practices to ensure ethical, responsible, and compliant AI implementations across the organization. Hands-On Leadership: Remain hands‑on in development, guiding the implementation of advanced machine learning, deep learning, and generative AI models and setting the standard for code quality. Drive Innovation: Champion research into emerging AI technologies, tools, and methodologies. Drive proof‑of‑concept initiatives and make strategic recommendations for integrating cutting‑edge solutions. Oversee MLOps: Define and lead the strategy for MLOps, including the robust deployment, monitoring, and full lifecycle management of AI applications in containerized environments like OpenShift. Required Skills and Qualifications 10+ years of relevant experience Leadership & Management: Proven experience in a technical leadership or team management role, with a strong ability to mentor engineers, manage project priorities, and drive team performance. Architectural Expertise: Demonstrated ability to design and architect scalable, high‑availability, end-to-end AI and machine learning systems. Business Acumen: Significant experience collaborating with non‑technical business stakeholders to identify opportunities, define requirements, and articulate a clear AI strategy. AI Governance: Strong understanding and practical experience in implementing governance, risk, and ethical guidelines for enterprise‑grade AI solutions. Expert Python Proficiency: Expert‑level programming skills in Python and its core AI/ML ecosystem (e.g., NumPy, Pandas, Scikit‑learn). Deep AI/ML Expertise: Extensive, hands‑on implementation experience with Machine Learning, Deep Learning, Generative AI, and Agentic AI systems. Advanced Data Expertise: Proven expertise in designing and managing complex data handling, preprocessing, and validation pipelines for mission‑critical AI applications. Advanced MLOps: Deep, practical experience in MLOps, including containerization (Docker), orchestration (OpenShift/Kubernetes), CI/CD, and production deployment of AI models. Education Bachelor’s/University degree, Master’s degree preferred Core Technical Stack & Expertise The ideal candidate will have deep, hands‑on expertise and strategic experience with the following technologies: LLMs: Gemini, OpenAI models (GPT series), Copilot, Claude, Llama, and experience with Local Models. Frameworks: LangChain, LlamaIndex, and the Hugging Face ecosystem (Transformers, Datasets, Tokenizers). Orchestration: LangGraph and designing and building complex Multi‑Agent Systems. Development: Building production‑grade, scalable services using Python, FastAPI, and asynchronous programming patterns. RAG (Retrieval‑Augmented Generation): Designing and implementing advanced retrieval techniques using Vector DBs (e.g., Pinecone, Chroma) and PostgreSQL (with pgvector). ML/DL Platforms: PyTorch and/or TensorFlow for building and fine‑tuning models at scale. Deployment & Monitoring: Containerization with Docker, deploying resilient production APIs, and implementing comprehensive monitoring, logging, and alerting strategies. Applied AI Skills: Mastery of Advanced Prompt Engineering, AI Workflow Design, and GenAI application optimization for cost, latency, and performance. The Ideal Candidate Strategic thinker and a master builder: capable of seeing the long‑term vision and diving deep into the technical details to make it a reality. Experienced and empathetic leader: who empowers your team to do their best work. Bridging the gap between business and technology: translating complex problems into actionable and impactful AI strategies. Deep commitment to building responsible AI: and establishing robust governance. Thought leader and lifelong learner: staying at the absolute cutting edge of the AI industry and inspiring others to do the same. Proven track record of delivering high‑impact AI solutions: from initial concept to full‑scale production. Citi is an equal opportunity employer, and qualified candidates will receive consideration without regard to their race, color, religion, sex, sexual orientation, gender identity, national origin, disability, status as a protected veteran, or any other characteristic protected by law. If you are a person with a disability and need a reasonable accommodation to use our search tools and/or apply for a career opportunity, review Accessibility at Citi. View Citi’s EEO Policy Statement and the Know Your Rights poster. #J-18808-Ljbffr