Senior Generative AI Engineer - Vice President

Job d:

Location:

Irving, Texas, United States Salary: $ - $Category:
Technology Company:
Citi 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 robust monitoring and logging.

Applied AI

Skills:

Advanced Prompt Engineering, AI Workflow Design, and GenAI application optimization for cost, latency, and performance.

Education:

Bachelor’s in Engineering degree, or equivalent work…