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.

• 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 Open Shift.

• 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., Num Py, 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 (Open Shift/Kubernetes), CI/CD, and production deployment of AI models.

• Bachelor’s/University degree;
  Master’s degree preferred.

• LLMs:
  Gemini, OpenAI models (GPT series), Copilot, Claude, Llama, and experience with Local Models.
• Frameworks:
  Lang Chain, Llama Index, and the Hugging Face ecosystem (Transformers, Datasets, Tokenizers).
• Orchestration:
  Lang Graph 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 Postgre
  
  SQL (with pgvector).
• ML/DL Platforms:
  PyTorch and/or Tensor Flow for building and…