Senior Engineer-AI & Full Stack Engineering Lead

Bank of America•Charlotte, NC

1d•Onsite

About The Position

At Bank of America, this role defines and leads the engineering approach for complex features that deliver significant business outcomes. The position provides technical thought leadership, enables development efficiencies, and applies broad and deep technical expertise across software implementations. It is accountable for end-to-end solution design and delivery, including architecture, development, testing, deployment, and production support. The role leads the design, development, and delivery of scalable enterprise applications and AI-enabled solutions across multiple business domains, ensuring alignment with enterprise security, architecture, and compliance standards. The engineer leads delivery of AI and non-AI software solutions across projects, builds backend systems using Java and Python, designs scalable architectures, and integrates AI capabilities such as LLMs, RAG, agents, and machine-learning pipelines into enterprise platforms. They translate business requirements into technical designs, promote engineering quality and best practices, manage CI/CD and production readiness, and provide technical leadership and mentorship. The role collaborates with stakeholders and architects to align technology delivery with business goals.

Requirements

10+ 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, and vector databases.
Experience with RESTful APIs and distributed systems.
Deep understanding of microservices, APIs, and event-driven architectures.
Understanding of cloud platforms; Azure is preferred.
Experience with containerization technologies such as Docker and Kubernetes.
Practical experience with LLM-based applications and prompt engineering.
Practical experience with model lifecycle management, including training, deployment, and monitoring.
Strong problem-solving and analytical thinking.
Excellent communication and stakeholder-management skills.
Ability to operate in a fast-paced, ambiguous environment.

Nice To Haves

Experience with AI governance, risk, and explainability, preferably in regulated industries.
Experience in financial services or other 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.

Responsibilities

Ensure the design and engineering approach for complex features is consistent with the larger portfolio solution.
Define the solution technology tool stack and evaluate or adapt testing tools, frameworks, and practices for teams.
Enable teams and applications with CI/CD capabilities and engage technical stakeholders to support efficient CI/CD pipeline operation.
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, design 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 architectures, including 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 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.