Job Summary:
The Context Engineering Manager serves as the context and knowledge lead for all DT&I product and process development, with primary emphasis on the knowledge, skill, and instruction infrastructure that AI systems and engineers consume. This role owns the design, governance, and continuous improvement of reusable skill libraries, agent-instruction standards (CLAUDE.md and equivalents), retrieval-grounding corpora, and domain-knowledge encodings that make AI-accelerated delivery reliable, repeatable, and audit-ready across the DT&I product portfolio. The role works in close coordination with the AI Engineering Manager and the Data & Analytics Lead to ensure assurance domain knowledge is captured as high-quality, governed, agent-consumable context. The Context Engineering Manager partners closely with the AI & Digital Innovation Delivery Lead and cross-functional teams to align context-engineering practices, knowledge standards, and product delivery with assurance service delivery objectives, firm policies, and security standards.
Job Duties:
Context Architecture & Knowledge Engineering
Designs and maintains the firm's context infrastructure including hierarchical skill libraries (foundation and archetype layers) and agent-instruction standards (CLAUDE.md and equivalents) with defined inheritance, ownership, and versioning
Serves as the principal technical authority on prompt and context patterns, reusable scaffolding, and context-as-code discipline across the DT&I portfolio
Defines how assurance domain knowledge is captured, structured, and surfaced to AI systems, and codifies BDO methodology (AKB) into retrievable, governed knowledge
Evaluates and integrates emerging context-engineering tools, frameworks, and knowledge platforms to continuously improve grounding quality and developer enablement
Designs context evaluation, provenance tracking, and citation discipline to ensure traceable, trustworthy grounded outputs
Retrieval & Grounding Engineering
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
Context Governance & Enablement
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
Risk Management
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
Supervisory Responsibilities:
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
Qualifications, Knowledge, Skills, and Abilities:
Education:
High School Diploma/GED, required
Bachelor's degree with a focus in Computer Science, Information Systems, Engineering, Information Technology, preferred
Experience:
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
License/Certifications:
Microsoft Certified: Azure AI Engineer Associate, or equivalent, preferred
Technology:
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... For full info follow application link.
All qualified applicants will receive consideration for employment without regard to race, age, color, religion, sex, national origin, disability, protected veteran status, or any other classification protected by law.
