Context Engineering Manager, Digital Transformation & Innovation

Context Engineering Manager

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.

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

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,…