Senior Engineer-AI & Full Stack Engineering Lead

Bank of America•Charlotte, NC

About The Position

At Bank of America, the Senior Engineer-AI & Full Stack Engineering Lead defines and leads the engineering approach for complex features that deliver significant business outcomes. The role provides technical thought leadership, improves development efficiency, and is accountable for end-to-end solution design and delivery. The position leads the design, development, and delivery of scalable enterprise applications and AI-enabled solutions across multiple business domains. It oversees architecture, development, testing, deployment, and production support, while ensuring alignment with enterprise security, architecture, and compliance standards. The role works with stakeholders to establish solution needs and with architects to define technical requirements, translating business goals into high-quality implementations. The engineer delivers AI and non-AI solutions, develops backend systems using Java and Python, integrates AI capabilities such as LLMs, RAG, agents, and ML pipelines, and drives engineering quality, testing, CI/CD, monitoring, and production readiness. The position also provides technical leadership and mentorship to engineers and aligns technology delivery with business goals. Bank of America describes an in-office culture intended to support collaboration, engagement, and career development, with flexibility based on role responsibilities and business needs. This position is listed as first shift, 40 hours per week.

Requirements

10 or more years of experience in software engineering and system design.
Proven experience delivering large-scale enterprise applications and AI solutions.
Strong expertise in Java, including Spring Boot and microservices.
Strong expertise in Python for AI/ML, APIs, and data engineering.
Hands-on experience with AI/ML frameworks such as OpenAI, Hugging Face, or LangChain.
Hands-on experience with RAG pipelines, embeddings, vector databases, RESTful APIs, and distributed systems.
Deep understanding of microservices, APIs, and event-driven architectures.
Experience with cloud platforms; Azure is preferred.
Experience with containerization using Docker and Kubernetes.
Practical experience with LLM-based applications and prompt engineering.
Practical experience with model lifecycle management, including training, deployment, and monitoring.
Ability to lead end-to-end solution delivery and provide technical leadership.
Skills in 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, and solution delivery process.

Nice To Haves

Experience in financial services or regulated industries.
Exposure to the Microsoft ecosystem, including Copilot Studio, Foundry, or Fabric.
Familiarity with agent orchestration, MCP, and AI platform integration patterns.
Experience with data privacy, compliance, and secure AI deployments.
Experience with AI governance, risk, and explainability, preferably in regulated industries.
Strong problem-solving and analytical thinking.
Excellent communication and stakeholder management.
Ability to operate in a fast-paced, ambiguous environment.

Responsibilities

Ensure the design and engineering approach for complex features is consistent with the larger portfolio solution.
Define the technology tool stack and evaluate or adapt testing tools, frameworks, and practices.
Enable teams and applications with CI/CD capabilities and work with technical stakeholders to support efficient CI/CD pipelines.
Guide teams on design and high-performance coding practices, including pairing and code reviews.
Deliver complex features end to end, including automation, for one or multiple teams at program level.
Conduct research, prototyping, and exploration of tools and components for release management, CI/CD, and features.
Work with stakeholders to establish high-level solution needs and with architects to define technical requirements.
Lead end-to-end delivery of AI and non-AI software solutions across multiple projects.
Design and implement scalable microservices, cloud-native, and event-driven architectures.
Build and maintain backend systems using Java and Python.
Develop and integrate AI solutions, including LLMs, RAG, agents, and ML pipelines, into enterprise platforms.
Translate business requirements into technical designs and high-quality implementations.
Ensure adherence to enterprise security, compliance, and performance standards.
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.