Context Engineering Manager, Digital Transformation & Innovation

• Owns retrieval-corpus curation and grounding quality including source selection, chunking strategy, embeddings, and index design using Azure AI Search and vector stores
• Designs and tunes retrieval pipelines (hybrid search, re-ranking, metadata filtering) for accuracy, cost, and latency across DT&I products
• Establishes corpus lifecycle management including freshness, versioning, deduplication, and retirement of stale knowledge
• Partners with the AI Engineering Manager to integrate grounded context into agent and application runtimes
• Implements grounding evaluation, regression testing, and quality metrics for retrieval-augmented features

• Governs the skill and context catalog as a managed asset with named ownership, review cadence, and change control consistent with the Advantage SDLC and Architecture Review Board
• Provides governed context scaffolding and standards for the Advantage Forge citizen-engineer program and product teams
• Coaches engineers on context-engineering practice and maintains documentation so AI-native development scales across the practice
• Defines standards for token economics, context-window management, and prompt efficiency across the portfolio

• Ensures context infrastructure and grounded knowledge comply with firm security policies, privacy requirements, and regulatory standards (SOC 2, PCAOB AS 2201, QC 1000, ISO 27001)
• Prevents sensitive or restricted data from entering prompts, corpora, or model context, and maintains auditability and traceability of grounded knowledge
• Partners with risk and compliance stakeholders to maintain alignment between context infrastructure and firm governance requirements

Performs other duties as assigned

• Willingly accepts share of less desirable assignments

• Acts as a direct supervisor to engineering and development team members, as assigned
• Acts as a career advisor and mentor to engineering and development team members as assigned

• High School Diploma/GED, required
• Bachelor’s degree with a focus in Computer Science, Information Systems, Engineering, Information Technology, preferred

• Seven (7) or more years of experience in software, data, or AI engineering, or related technology fields, required
• Three (3) or more years of experience building LLM context, retrieval-augmented generation (RAG), or knowledge-management systems, required
• Two (2) or more years of experience defining reusable engineering assets, developer-enablement tooling, or technical standards, required
• Experience extracting and codifying domain knowledge into machine-consumable formats, preferred
• Experience delivering enterprise-scale AI or knowledge solutions in professional services, assurance, or accounting industries, preferred
• Experience with context governance, prompt management, or AI evaluation frameworks, preferred

• Microsoft Certified: Azure AI Engineer Associate, or equivalent, preferred

• Expert-level knowledge of Microsoft Azure AI services including Azure AI Foundry, Azure OpenAI, and Azure AI Search, required
• Strong understanding of LLM context windows, prompting, retrieval, and grounding, and how each affects accuracy, cost, and reliability, required
• Experience with retrieval and vector technologies (Azure AI Search, embeddings, hybrid search, re-ranking), required
• Extensive knowledge of agent-instruction and skill systems (CLAUDE.md and equivalents) and Markdown-based knowledge structures, required
• Experience with agentic development tooling (Claude Code or equivalent) and Git-based workflows, preferred
• Experience with.NET/C# and/or Python for context and evaluation tooling, preferred
• Experience with AI evaluation and observability tooling, preferred

• Expert-level knowledge of context engineering, retrieval design, and grounding for production LLM systems
• Proven ability to serve as a hands-on context architect while providing standards, governance, and team enablement
• Deep knowledge of information architecture and the ability to translate subject-matter expertise into machine-consumable knowledge
• Familiarity with AI application and agent runtimes and the ability to collaborate with engineering teams on context dependencies
• Excellent problem-solving skills with a focus on accuracy, reliability, and grounding quality
• Strong communication skills with the ability to convey technical concepts to non-technical stakeholders and leadership
• Experience presenting context and AI governance recommendations to governance bodies (e.g., Architecture Review Boards)
• Strong knowledge of data privacy, security, and compliance considerations for AI and knowledge systems
• Ability to work across technical teams and business stakeholders in complex, fast-paced enterprise environments
• Self-directed with the ability to manage multiple priorities and competing deadlines
• Collaborative mindset with the ability to build relationships across all levels of the organization
• Ability to travel up to 10%, no relocation required

Individual salaries that are offered to a candidate are determined after consideration of numerous factors including but not limited to the candidate’s qualifications, experience, skills, and geography.

National Range: $125,000 - $155,000 Maryland Range: $125,000 - $155,000 NYC/Long Island/Westchester Range: $125,000 - $155,000