SUMMARY: The AI Architect Lead is a leadership role which will entail both strategy as well as more tactical technology assessment. From a strategy position, this role will engage with various levels of the organization (department heads, team leads, executive management, risk & compliance, etc.) to lead the development of potential AI use cases which will directly influence the strategic direction of the Bank's adoption of AI. The AI Architect Lead will report to the Chief Technology Officer and in doing so will play an integral role in maturing the Bank's AI framework by providing thought leadership and oversight for AI-enabled application design and development. This role will also work with various business teams to understand their goals and requirements while conversely engaging technical engineering teams to align how AI would be best leveraged to solve the business problem and/or to achieve the desired outcome. Assistance in the definition of AI architecture frameworks and patterns for reuse as well as engagement with Risk Management teams to help define AI processes and controls will also be a key function.
ESSENTIAL DUTIES AND RESPONSIBILITIES include the following. Other duties and special projects may be assigned.
- Collaborate with key stakeholders to identify opportunities for using AI to enhance business processes and achieve desired outcomes.
- Work across engineering teams to recommend suitable AI technologies and solutions to address identified opportunities and to leverage synthetic data to address data drift, data quality, and mitigate bias in AI solutions.
- Establish a deep understanding of our business needs and communicate key insights/findings/requirements to engineering teams.
- Conduct PoCs to demonstrate the value and feasibility of AI solutions to the lines of business.
- Advise on the selection and implementation of appropriate AI technologies based on business needs and technical considerations.
- Perform risk assessments of AI solutions, identifying potential risks and recommending effective mitigation strategies.
- Translate the vision of business requirements into realistic technical implementations, while identifying misaligned initiatives and impractical use cases
- Contribute toward the development of a comprehensive AI Architecture framework inclusive of patterns and guidelines that support the deployment, evaluation, and management of AI solutions
- Contribute to the definition of the Bank's AI/ML technical direction and architectural vision, ensuring alignment with strategic goals and AI transformation efforts.
- Assist toward the selection of appropriate AI technologies from a pool of open-source and commercial offerings, considering deployment models and integration with existing tools
- Understand LLM model architecture and evaluate its use (embedded or otherwise) as part of 3rd party platform evaluation
- Contribute to the successful execution of AI-powered applications using an agile methodology
- Work closely with security and risk leaders to identify and mitigate risks, ensuring ethical AI implementation and compliance with upcoming regulations
- Work with the HR training department to develop and roll out appropriate AI training.
- Help AI product managers and business stakeholders to understand the potential and limitations of AI when planning new products
- Building positive relationships with various line of business to understand their goals and objectives
- Assist toward the preparation of AI Architecture design for Architecture Review Board approval
- Identification/research of forward-looking AI use-cases and emerging technologies that will drive the AI roadmap for the bank, covering ML, advanced LLM architectures that provided competitive advantage
- Provide leadership, oversight and guidance on AI application design and development
- Contribute to the establishment of technology standards, best practices for data privacy, model security, and compliance for AI including ML, Gen AI lifecycle management
- Spearhead the design and integration of Generative AI applications including prompt engineering, retrieval augmented generation (RAG), fine-tuning(LoRA, QoRA), and multimodal AI for Enterprise use.
- Lead the design and implementation of scalable MLOps/LLMOps frameworks ensuring robust model lifecycle management, CI/CD and automated model monitoring.
- Collaborate with Cloud Architecture, Security Architecture, Enterprise Architecture in aligning the AI Solutions with existing Enterprise standards, tech stacks, security policies, and cost optimized cloud best practices for deploying, scaling, and swapping LLMs
- Adheres to and complies with applicable, federal and state laws, regulations and guidance, including those related to anti-money laundering (i.e. Bank Secrecy Act, US PATRIOT Act, etc.).
- Adheres to Bank policies and procedures and completes required training.
- Identifies and reports suspicious activity.
EDUCATION
Bachelor's Degree in any of Computer Science, Engineering, Statistics, Data Science or a related field required
EXPERIENCE
- 7-9 Years years of experience translating business requirements into technical requirements and design documents required
- 7-9 Years years of experience with application architecture analysis, design, and delivery required
- 4-6 Years years of full application development life cycle implementations required
- 1-3 Years Experience working with Traditional AI and GenAI Technologies required
- 1-3 Years Experience in a project team lead role and road mapping exercises required
- 4-6 Years Banking/Financial background or experience required
CERTIFICATES, LICENSES, REGISTRATIONS
- Relevant certifications in Cloud AI platforms such as AWS, GCP, Azure, Oracle and data technologies a plus preferred
KNOWLEDGE, SKILLS AND ABILITIES
- Excellent communication skills and business acumen
- Ability to communicate technical topics in a clear, succinct, and relevant way.
- Working knowledge of AI / ML frameworks and tools
- Working knowledge of data science, data engineering, and software development principles.
- Consistently demonstrate professional, positive, and approachable attitude, demeanor and discretion.
- Agentic AI, Model Context Protocols (MCP), LLM model fine-tuning methods such as LoRA, QoRA
- Claude Code, Frontier LLM models - GPT, Claude, Gemini, Github Copilot, VS Code, Python
- GPU architecture, KV Cache, Token economics
- AWS Bedrock, M365 Copilot Studio, M365 Copilot, LangChain, Autogen, Azure AI Foundry
ADDITIONAL INFORMATION
- Candidates residing in locations within BankUnited's footprint may be given preference.
Candidates residing in locations within BankUnited's footprint may be given preference.
