Senior Generative AI & Testing Efficiency Developer, Vice President

Citi•Irving, TX

1d•Onsite

About The Position

Citi, a leading global bank with approximately 200 million customer accounts across more than 160 countries, provides a broad range of financial products and services. Its Enterprise Operations & Technology teams are tasked with a mission comparable to large tech companies, focusing on bank safety, global resource management, providing technical tools, designing digital architecture, and ensuring a first-class customer experience. The Senior Generative AI & Testing Efficiency Developer is a senior-level role responsible for leading the design, development, and implementation of complex application systems. This position involves close partnership with technology, architecture, QA, and business teams to drive system enhancements, modernize platforms, and deliver high-impact solutions, with a specific emphasis on advancing AI-powered and generative AI–driven testing capabilities across the enterprise.

Requirements

Strong hands-on experience with LLM development, fine-tuning, and optimization.
Expertise in RAG systems, hybrid search, and vector retrieval.
Proficiency with ML frameworks such as PyTorch, TensorFlow, and Keras, including distributed training.
Experience with GenAI tools and libraries including LangChain, LlamaIndex, LangGraph, Crew.ai, Autogen, Hugging Face, and cloud GenAI APIs (e.g., OpenAI, Claude, Gemini).
Strong understanding of test automation frameworks (Selenium, Playwright).
Experience integrating AI capabilities into testing pipelines (AI-generated tests, self-healing scripts, test impact analysis).
Solid knowledge of CI/CD systems with a focus on continuous testing.
Familiarity with QA methodologies, test strategy, functional and non-functional testing, and quality metrics.
Advanced Python skills for automation tooling, data preprocessing, API development, and AI workflows.
Experience with containerization and deployment technologies (Docker, Kubernetes).
Practical knowledge of model optimization techniques.
Strong understanding of AI governance, model guardrails, testing risk frameworks, and Responsible AI practices.
Excellent collaboration and communication skills, with the ability to explain AI-driven testing strategies to both technical and non-technical audiences.
Analytical, proactive mindset with a passion for experimentation, innovation, and mentoring.
6+ years of relevant experience in applications development or systems analysis.
Extensive experience in systems analysis and software application development.
Proven success leading and delivering complex projects.
Subject Matter Expert (SME) in at least one area of applications development.
Demonstrated leadership, adaptability, and project management skills.
Consistently strong written and verbal communication skills.
Bachelor’s degree or equivalent practical experience required.

Nice To Haves

Master’s degree preferred.

Responsibilities

Lead applications systems analysis, design, and programming activities in alignment with Citi’s enterprise architecture.
Partner with multiple management teams to integrate systems, deploy new products, and enable process improvements.
Resolve high-impact, complex problems through deep analysis of business processes, system flows, and industry standards.
Establish standards for coding, testing, debugging, and implementation.
Develop a strong understanding of how architecture, infrastructure, and applications integrate to achieve business goals.
Provide technical mentorship and coaching to mid-level developers and analysts; allocate work as needed.
Assess and manage risk in all technical and business decisions, ensuring compliance with laws, regulations, internal policies, and ethical standards.
Design and implement AI-powered testing solutions, including AI‑generated test cases, intelligent regression selection, self‑healing automation, and automated defect and failure analysis.
Develop and optimize LLM-based tools for test data generation and scenario creation, requirements-to-test traceability, and predictive test failure analysis.
Embed generative AI capabilities into existing automation frameworks (e.g., Selenium, Playwright).
Implement advanced GenAI techniques such as prompt engineering and Retrieval‑Augmented Generation (RAG) to enhance testing intelligence.
Integrate AI-driven testing accelerators into CI/CD pipelines to reduce cycle time and improve stability.
Support deployment, scalability, monitoring, and optimization of AI models in production environments.
Contribute to real-time and streaming AI systems that enable continuous testing and rapid feedback loops.
Ensure compliance with Responsible AI principles, data privacy requirements, governance standards, and quality controls.
Stay current with emerging trends in generative AI and test automation; evangelize best practices across the organization.
Mentor junior engineers and QA automation developers on AI-assisted testing methodologies.