Context Engineering Manager, Digital Transformation & Innovation

Company: BDO USA, P.C.

Role summary

Context Engineering Manager, Digital Transformation & Innovation is a full-time role at BDO USA, P.C. in Grand Rapids, Michigan, United States (on-site). Posted 2026-08-24. Experience level: 5-10.

Job title
Context Engineering Manager, Digital Transformation & Innovation
Company
BDO USA, P.C.
Location
Grand Rapids, Michigan, United States
Work arrangement
on-site
Employment type
full-time
Category
Software Engineering
Experience level
5-10
Date posted
2026-08-24

You’ll be at the heart of BDO’s digital evolution, helping bridge the gap between complex business needs and cutting-edge technical solutions. It is a fantastic opportunity for someone who enjoys shaping strategy and leading teams through large-scale transformations, all while working in a collaborative environment that rewards innovation and creative problem-solving.

Job description

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 with .NET/C# an

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