Principal Engineer - Context Engineering & LLM Optimization

Job description

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 solutions at the program or portfolio level, to deliver significant business outcomes. Key responsibilities include continuously improving the design, quality, and reuse of the solution and delivering technology enablers that improve development efficiencies for the solution. Job expectations include familiarity with at least one area of engineering, acting as a “go to” reference across the organization, and applying knowledge to improve technical competencies through recruitment and development activities. Developer Experience (DevEx) provides enterprise technical standards and common technical services, platforms, and tools that are leveraged by delivery teams across all lines of business. Within the SDLC Software Delivery Lifecycle program, this role leads portfolio product delivery strategy and execution for enterprise software delivery capabilities, ensuring the right investments, operating model, governance, and prioritization are in place to improve how internal technical users build, test, and deliver software at scale. The Context Engineering & LLM Optimization Principal Engineer is responsible for designing how information is selected, organized, compressed, prioritized, and presented to LLMs. This role focuses on context window management, prompt architecture, retrieval orchestration, grounding strategies, instruction design, tool-use patterns, and evaluation of LLM behavior. This engineer ensures that LLM applications receive the right context, in the right format, at the right time, with minimal token waste and maximum answer quality. Responsibilities: Develops the engineering approach for the entire program/portfolio solution and works with Architecture, to develop/analyze/deliver the implementation of technical enablers Leads the planning, definition, and design of the complex features which span multiple teams and explore solution alternatives Creates ideas on designing complex technology and solution development approaches Leads the technical oversight for teams in solution development including design reviews and code within own domain Defines the technology tool stack for the solution within ranged of internally approved and supported technologies Explores state-of-the-art technologies to improve development efficiencies, quality of test/QA coverage, and release management Leads and is responsible for the end-to-end test strategy/creation/adherence, and the integration between teams for a program/portfolio solution Design context engineering strategies for enterprise LLM and RAG applications. Define prompt architectures for system prompts, developer instructions, user prompts, retrieved context, tool outputs, conversation history, and structured constraints. Optimize context window usage through summarization, compression, ranking, filtering, deduplication, and context prioritization. Design retrieval orchestration patterns that determine what data is retrieved, when it is retrieved, and how it is injected into the LLM prompt. Partner with RAG database engineers to tune retrieval outputs for downstream reasoning quality. Partner with data ingestion engineers to improve source formatting, metadata, and chunk structures for better contextual use. Develop patterns for multi-turn conversation memory, session state, user intent preservation, and context refresh. Define strategies for grounding, citation handling, source attribution, conflicting evidence resolution, and hallucination reduction. Improve the experience for our developers, making it easier to deliver industry-leading solutions, while managing work efficiently and with the right controls Advance our technology platforms through innovation Reduce risk and improve quality across our technology portfolio by aligning to a single enterprise architecture strategy and delivering governance that enables consistency, integration and automation Design LLM evaluation frameworks for answer quality, factuality, instruction adherence, relevance, safety, and token efficiency. Establish prompt engineering and context engineering standards across product and platform teams. Evaluate LLM model behavior across different context sizes, retrieval strategies, and prompt structures. Define reusable patterns for agents, tool calling, function calling, dynamic prompt generation, and workflow-based reasoning. Lead technical reviews for LLM application design, prompt safety, and context efficiency. Serve as a senior technical authority for enterprise AI platform engineering. Own architecture decisions that impact multiple teams, systems, or domains. Create reusable patterns, reference architectures, standards, and engineering guardrails. Mentor senior engineers and influence technical direction without requiring direct reporting authority. Balance innovation with operational reliability, security, compliance, scalability, and cost management. Communicate complex AI and data engineering concepts clearly to engineering, product, risk, security, and executive stakeholders. Required Qualifications: 10+ years of software engineering, data engineering, platform engineering, or AI engineering experience. 5+ years designing large-scale enterprise systems. 2+ years working with LLM, RAG, vector search, semantic search, or AI platform capabilities. Experience operating systems in regulated, security-conscious, or enterprise-scale environments. Extensive experience building or architecting production LLM, RAG, or AI assistant systems. Deep understanding of how LLMs use prompts, retrieved context, conversation history, system instructions, and tool outputs. Strong knowledge of context window management, token budgeting, prompt construction, grounding, and response evaluation. Experience with OpenAI, Azure OpenAI, Anthropic, Google Gemini, Meta Llama, or similar LLM ecosystems. Experience designing prompt templates, retrieval-augmented prompts, agent workflows, and tool-use orchestration. Familiarity with vector search, embeddings, reranking, semantic retrieval, and document chunking. Experience with automated LLM evaluation, prompt regression testing, and quality measurement. Ability to define enterprise standards for reliable, explainable, and secure LLM behavior. Proven ability to lead architecture across multiple engineering teams. Strong written and verbal communication skills. Bachelor’s degree in Computer Science, Engineering, Information Systems, Applied Mathematics, or a related technical field Desired Qualifications: Experience with agentic workflows, multi-agent orchestration, function calling, or tool-augmented reasoning. Experience with prompt injection mitigation, jailbreak resistance, and secure context handling. Experience with token optimization, long-context models, summarization pipelines, and contextual compression. Experience with user personalization, enterprise memory patterns, or domain-specific copilots. Higher-quality LLM responses with better grounding and reduced hallucination. Lower token usage and improved response latency through efficient context construction. Standardized prompt and context patterns reused across teams. Improved evaluation coverage for LLM behavior, factuality, and instruction adherence. Better alignment between retrieved enterprise knowledge and generated responses. Enterprise architecture Distributed systems design AI platform engineering Data governance and security Cloud-native engineering Observability and operational excellence Technical strategy and roadmap development Cross-functional influence Vendor and platform evaluation Production support and continuous improvement 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: 1st shift (United States of America) Hours Per Week: 40