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 models to solve complex business problems.
• Build Robust Systems: Develop and maintain clean, efficient, and scalable code for AI/ML systems, with a focus on production‑level quality.
• Manage Data Pipelines: Engineer and manage sophisticated data handling and preprocessing pipelines to ensure high‑quality data for training and inference.
• Deploy & Operate: Utilize MLOps best practices to deploy AI applications in containerized environments like Open Shift, ensuring robust monitoring, scalability, and reliability.
• Innovate & Research: Actively monitor and research the latest trends, breakthroughs, and tools in AI/ML. Present findings and lead proof‑of‑concept projects to integrate 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., Num Py, 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 of and hands‑on experience with Generative AI, Large Language Models (LLMs), and Agentic AI frameworks.
• Data Expertise: Proven ability in handling and preprocessing large and complex datasets, including data cleaning, feature engineering, and validation.
• MLOps Awareness: Working experience or strong familiarity with MLOps principles, including containerizing applications using Docker and deploying on platforms like Open Shift or Kubernetes.
• Problem‑Solving: Strong analytical and problem‑solving skills with the ability to tackle complex challenges independently.

Core Technical Stack & Expertise:

The ideal candidate will have hands‑on experience or deep familiarity with the following technologies:

• 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 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 Tensor Flow for building and fine‑tuning models.
• Deployment & Monitoring: Containerization with Docker, deploying production APIs, and implementing…