AI Full Stack Engineering Lead

Job Description:

At Bank of America, we are guided by a common purpose to help make financial lives better through the power of every connection. We do this by driving Responsible Growth and delivering for our clients, teammates, communities and shareholders every day.

Being a Great Place to Work and providing a culture of caring is core to how we drive Responsible Growth. We are intentional about fostering an inclusive workplace where every teammate has the opportunity to succeed, build a career and contribute to our shared success. This includes attracting and developing exceptional talent, recognizing and rewarding performance, and supporting our teammates’ physical, emotional, and financial wellness through affordable, competitive and flexible benefits.

We value the unique perspectives individuals bring from all backgrounds and career paths - whether shaped by military service, community college education, or a wide range of work and life experiences. These journeys foster resilience, leadership and innovation, strengthening our workforce and positively impact the communities we serve.

Bank of America is committed to an in-office culture that supports collaboration, engagement, and career development. Our approach includes clear in-office expectations, while providing an appropriate level of flexibility based on role-specific responsibilities and business needs.

At Bank of America, you can build a successful career with opportunities to learn, grow, and make an impact. Join us!

Job Description:
This job is responsible for defining and leading the engineering approach for complex features to deliver significant business outcomes. Key responsibilities of the job include delivering complex features and technology, enabling development efficiencies, providing technical thought leadership based on conducting multiple software implementations, and applying both depth and breadth in a number of technical competencies.

- Lead design, development, and deployment of AI and non-AI applications across multiple business domains
- Own delivery accountability across planning, execution, testing, and production rollout
- Ensure alignment with enterprise architecture, security, and compliance standards
- Design and implement AI-powered solutions, including: Large Language Models (LLMs), prompt engineering, Retrieval-Augmented Generation (RAG) pipelines, Agent-based architectures and orchestration frameworks
- Integrate AI capabilities into enterprise systems via APIs and microservices
- Evaluate and adopt emerging AI technologies (e.g., Copilot, Foundry, open-source frameworks)
- Develop scalable backend services using: Java (Spring Boot, Microservices architecture), Python (FastAPI, data pipelines, AI/ML frameworks)
- Build and optimize high-performance, resilient, and maintainable systems
- Ensure best practices in coding standards, testing, and code reviews
- Define solution architectures for complex systems involving: Distributed systems and microservices, Event-driven architectures, Cloud-native patterns (Azure/AWS/GCP)
- Ensure system observability (logging, monitoring, alerting)
- Drive production support readiness, including incident resolution and RCA
- Provide technical guidance to engineering teams
- Conduct design reviews and mentor junior/mid-level engineers
- Promote engineering best practices and continuous learning
- Partner with product owners, architects, and business stakeholders to translate requirements into technical solutions
- Communicate complex technical concepts to both technical and non-technical audiences
- Contribute to strategic initiatives and roadmap planning

Responsibilities:

• Lead end-to-end delivery of AI and non-AI software solutions across multiple projects

• Design and implement scalable architectures (microservices, cloud-native, event-driven)

• Build and maintain backend systems using Java and Python

• Develop and integrate AI solutions (LLMs, RAG, agents, ML pipelines) into enterprise platforms

• Translate business requirements into technical designs and high-quality implementations

• Ensure adherence to enterprise standards for security, compliance, and performance

• Drive code quality, testing, and engineering best practices across teams

• Implement and manage CI/CD pipelines, monitoring, and production readiness

• Provide technical leadership and mentorship to engineers and review solution designs

• Collaborate with stakeholders to align technology delivery with business goals

• Create and review technical design documents, architecture diagrams, and standards

• Drive reusability, modularity, and scalability across solutions

• Design and manage data ingestion, transformation, and processing pipelines

• Work with structured and unstructured data, including financial and operational datasets

• Implement feature engineering, model deployment, and monitoring pipelines

Required Qualifications:

• 10+ years of experience in software engineering and system design

• Proven experience delivering large-scale enterprise applications and AI solutions

• Strong expertise in: Java (Spring Boot, Microservices), Python (AI/ML, APIs, data engineering)

• Hands-on experience with:

• AI/ML frameworks (OpenAI, Hugging Face, LangChain,etc.)

• RAG pipelines, embeddings, vector databases

• RESTful APIs, distributed systems

• Deep understanding of:

• Microservices, APIs, event-driven architectures

• Cloud platforms (Azure preferred)

• Containerization (Docker, Kubernetes)

• Practical experience with:

• LLM-based applications and prompt engineering

• Model lifecycle management (training, deployment, monitoring)

• AI governance, risk, and explainability (preferred in regulated industries)

• Strong problem-solving and analytical thinking

• Excellent communication and stakeholder management

• Ability to operate in a fast-paced, ambiguous environment

Desired Qualifications:

• Experience in financial services or regulated industries

• Exposure to Microsoft ecosystem (Copilot Studio, Foundry, Fabric)

• Familiarity with agent orchestration, MCP, and AI platform integration patterns

• Experience with data privacy, compliance, and secure AI deployments​

Skills:

• Automation

• Influence

• Result Orientation

• Stakeholder Management

• Technical Strategy Development

• Application Development

• Architecture

• Business Acumen

• Risk Management

• Solution Design

• Agile Practices

• Analytical Thinking

• Collaboration

• Data Management

• Solution Delivery Process

Shift:

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