AI Full Stack Engineering Lead

Bank of America Bank of America · Banking · Charlotte, NC

Lead the engineering for AI and non-AI applications, focusing on LLMs, RAG, and agent-based architectures. Develop scalable backend services using Java and Python, integrate AI capabilities, and ensure system observability. Requires strong experience in software engineering, system design, AI/ML frameworks, RAG, vector databases, and LLM applications.

What you'd actually do

  1. Lead design, development, and deployment of AI and non-AI applications across multiple business domains
  2. Own delivery accountability across planning, execution, testing, and production rollout
  3. Ensure alignment with enterprise architecture, security, and compliance standards
  4. Design and implement AI-powered solutions, including: Large Language Models (LLMs), prompt engineering, Retrieval-Augmented Generation (RAG) pipelines, Agent-based architectures and orchestration frameworks
  5. Integrate AI capabilities into enterprise systems via APIs and microservices

Skills

Required

  • 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.)
  • Hands-on experience with: 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)
  • Strong problem-solving and analytical thinking
  • Excellent communication and stakeholder management
  • Ability to operate in a fast-paced, ambiguous environment

Nice to have

  • Experience in financial services or regulated industries
  • AI governance, risk, and explainability
  • Copilot, Foundry, open-source frameworks

What the JD emphasized

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

Other signals

  • Lead design, development, and deployment of AI and non-AI applications
  • 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
  • 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 system observability (logging, monitoring, alerting)
  • Provide technical guidance to engineering teams
  • Partner with product owners, architects, and business stakeholders to translate requirements into technical solutions
  • Develop and integrate AI solutions (LLMs, RAG, agents, ML pipelines) into enterprise platforms
  • Implement feature engineering, model deployment, and monitoring pipelines
  • Hands-on experience with: AI/ML frameworks (OpenAI, Hugging Face, LangChain,etc.)
  • Hands-on experience with: RAG pipelines, embeddings, vector databases
  • Practical experience with: LLM-based applications and prompt engineering
  • Model lifecycle management (training, deployment, monitoring)