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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior Generative AI Engineer - Vice President - **Company:** Citi - **Location:** Irving, TX, United States - **Experience:** Expert - **Salary:** $125,760.0 - $188,640.0 - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Data Analysis, Big Data, Computer Programming, Data Cleansing, Python (Programming Language), PostgreSQL, Machine Learning, NumPy, OpenShift, Tensorflow, Software Engineering, Workflow Management Systems, Data Logging, Data Processing, Feature Engineering, Pytorch, Retrieval-Augmented Generation, Large Language Models, Multi-Agent Systems, Prompt Engineering, Deep Learning, Generative AI, Fastapi, Pandas, Containerization, Scikit Learn, Kubernetes, Deployment Automation, HuggingFace, Machine Learning Operations, Virtual Agents, Asynchronous Programming, GPT, Data Pipelines, Docker - **Published:** September 5, 2026 - **Apply:** https://citi.wd5.myworkdayjobs.com/2/job/Irving-Texas-United-States/Senior-Generative-AI-Engineer---Vice-President_26990493-1/apply ## About the Role * 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 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 OpenShift 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: LangChain, LlamaIndex, 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, and GenAI application optimization for cost, latency, and performance. Education: * Bachelor's in Engineering degree, or equivalent work experience ## Description * 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 OpenShift, 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. ## Related Videos - [ Evaluating AI models for code comprehension](https://www.wearedevelopers.com/videos/1462-evaluating-ai-models-for-code-comprehension) - [Vectorize all the things! 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