AI Full Stack Engineering Lead

Bank of America

Charlotte (NC)

On-site

USD 140,000 - 190,000

Full time

14 days+

Get more replies from employers

Send a job-specific resume in minutes.

Job summary

Bank of America is seeking an experienced software engineering leader to define and drive AI-enabled features across complex domains. You will own the engineering approach, deliver high-impact technology, and mentor teams to deliver robust AI and non-AI solutions.

The role focuses on building scalable backend services in Java and Python, integrating AI capabilities into enterprise systems, and guiding architectural decisions while aligning with security and compliance standards.

Qualifications

10+ years of software engineering and system design.
Proven experience delivering large-scale enterprise apps and AI solutions.
Strong Java (Spring Boot, Microservices) and Python (AI/ML, APIs).
Hands-on with AI/ML frameworks (OpenAI, HF, LangChain).
RAG pipelines, embeddings, vector databases.
RESTful APIs and distributed systems.
Cloud platforms (Azure preferred).
Docker/Kubernetes containerization.
LLM-based apps and prompt engineering.
Model lifecycle management.

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.

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

Tools

Docker

Kubernetes

OpenAI

Hugging Face

LangChain

Job description

Company Overview

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