Senior Generative AI Engineer - Vice President

The USCC Architecture and AI Engineering group is at the forefront of technological innovation, and we are looking for a highly motivated and talented Senior Generative AI/AI Engineer to join our dynamic team. This is an exciting opportunity to work on cutting-edge AI solutions that will shape the future of our industry. 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 expert collaborate effectively with business stakeholders, and steer our technical direction in the rapidly evolving landscape of Generative and Agentic AI. Responsibilities: Accountability : Executing and driving results on large-scale AI efforts or multiple smaller AI efforts and serving as a development lead for most medium and large AI projects. This includes expertise with application development methodologies, generative AI & AI and standards for program analysis, design, coding, testing, debugging and implementation. Develop & Prototype: Design, build, and iterate on prototypes for Generative and Agentic AI applications with speed and agility, demonstrating the art of the possible. Implement AI Models: Implement, train, and fine-tune a variety of machine learning and deep learning mod [... source excerpt omitted ...] ate new technologies into our stack. Collaborate: Work closely with senior engineers, architects, and product managers in a highly collaborative environment to translate business requirements into technical solutions. Required Skills and Qualifications : Atleast 6+ years of relevant experience Python Proficiency: Strong and efficient programming skills in Python, including deep familiarity with AI-centric libraries (e.g., NumPy, Pandas, Scikit-learn). ML/DL Foundation: Solid understanding and practical implementation experience with machine learning algorithms and deep learning architectures (e.g., Transformers, CNNs, RNNs). Generative AI Experience: Demonstrable understanding [... source excerpt omitted ...] and the Hugging Face ecosystem (Transformers, Datasets, Tokenizers). Orchestration: LangGraph and conceptual understanding of building Multi-Agent Systems. Development: Building production-ready services using Python, FastAPI, and asynchronous programming patterns. RAG (Retrieval-Augmented Generation): 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. Deployment & Monitoring: Containerization with Docker, deploying production APIs, and implementing robust monitoring and logging. Applied AI Skills: Advanced Prompt Engineering, AI Workflow Design