Bank Computer Science Jobs in McKinney, TX (NOW HIRING)

This role is ideal for a highly technical and hands-on AI leader who thrives at the intersection of research, innovation, and execution. The successful candidate will continuously evaluate emerging advances in artificial intelligence, identify opportunities to create business value, rapidly prototype novel solutions, and lead their evolution into scalable enterprise capabilities.

Unlike traditional research positions, this role spans the entire R&D lifecycle. The ideal candidate combines deep scientific expertise with strong software engineering and solution delivery capabilities, enabling them to move seamlessly from research exploration and experimentation to deployment and adoption of production-grade AI solutions.

The candidate is expected to be an active contributor to the broader AI community, maintain awareness of cutting-edge academic and industry developments, and help shape the future direction of AI innovation within the bank.

AI Research & Innovation

• Monitor, evaluate, and experiment with emerging AI technologies, research breakthroughs, and industry trends, with particular emphasis on generative AI, large language models (LLMs), multimodal AI, and agentic AI systems.

• Identify opportunities to apply advanced AI techniques to complex business problems across financial services.

• Conduct original AI research and exploratory investigations to assess the feasibility and value of novel approaches.

• Develop hypotheses, design experiments, evaluate results, and communicate findings to technical and executive stakeholders.

• Contribute to publications, patents, technical whitepapers, and thought leadership initiatives where appropriate.

Prototyping & Solution Development

• Rapidly transform research concepts into working prototypes and proof-of-concepts.

• Design, develop, and validate AI solutions across the full lifecycle, from data preparation and modeling through deployment and monitoring.

• Build experimental and production-ready solutions using modern AI/ML tools, frameworks, and cloud platforms.

• Apply best practices in model evaluation, benchmarking, explainability, security, and responsible AI.

Generative AI & Agentic Systems

• Lead the development of solutions leveraging foundation models, generative AI, retrieval-augmented generation (RAG), fine-tuning techniques, and agentic workflows.

• Design and evaluate AI agents, multi-agent systems, orchestration frameworks, tool-use architectures, memory systems, and human-in-the-loop workflows.

• Establish best practices for prompt engineering, model adaptation, evaluation, observability, and governance of LLM-based systems.

• Assess emerging model architectures and determine their applicability within a highly regulated environment.

Deployment & Technical Leadership

• Partner with software engineers, platform teams, product managers, and business stakeholders to transition successful prototypes into production environments.

• Provide technical leadership throughout deployment and operationalization activities.

• Drive architectural decisions for AI solutions while balancing innovation, scalability, security, compliance, and operational requirements.

• Mentor engineers and data scientists and provide guidance on advanced AI techniques and best practices.

• Take ownership of outcomes and ensure successful delivery of AI capabilities from concept through adoption.

Basic Qualifications

• Bachelor's degree, or equivalent work experience

• Eight or more years of relevant experience

Preferred Skills/Experience

• Master's degree or Ph.D. in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Applied Mathematics, Statistics, or a related technical discipline.

• 8+ years of experience in AI/ML research, development, and deployment.

• Strong programming skills in Python and modern AI/ML frameworks.

• Demonstrated experience designing, building, and deploying AI/ML solutions in production environments.

• Experience working across the full lifecycle of AI product development, from research and experimentation through deployment and operationalization.

• Deep expertise in machine learning, deep learning, neural networks, transformer architectures, and foundation models.

• Extensive experience with generative AI technologies, including LLMs, fine-tuning, RAG systems, model evaluation, and inference optimization.

• Experience building and evaluating agentic AI systems, multi-agent workflows, AI orchestration frameworks, and autonomous decision-making architectures.

• Strong hands-on experience with PyTorch, Hugging Face, Langchain, LangGraph, or equivalent AI ecosystems.

• Demonstrated record of innovation through publications, patents, open-source contributions, conference presentations, or significant AI solution delivery.

• Experience developing AI solutions within highly regulated industries such as financial services, healthcare, insurance, or telecommunications.

• Strong communication skills and the ability to translate complex AI concepts into actionable business outcomes.