Sets and enforces engineering practice for the team’s Git-based workflows, pull-request review, automated testing, and CI/CD pipelines (Azure DevOps or GitHub), and uses agentic development tooling (Claude Code or equivalent) as part of daily delivery
Reviews code and context contributions from other engineers for correctness, quality, security, and reuse
Context & Knowledge Engineering
Designs and builds complex, reusable context assets—skill libraries, agent-instruction files (CLAUDE.md and equivalents), and prompt and context patterns—for use across DT&I products
Serves as a senior technical resource on context-as-code practice, establishing conventions, inheritance, and versioning standards for the team
Codifies BDO assurance domain knowledge (AKB) into structured, retrievable, governed knowledge with appropriate provenance and citation discipline
Evaluates and pilots emerging context-engineering tools and frameworks to improve grounding quality and developer productivity
Retrieval & Grounding Engineering
Designs and tunes retrieval pipelines (document parsing, chunking strategy, embeddings, hybrid search, re-ranking, metadata filtering) for accuracy, cost, and latency using Azure AI Search and vector stores
Owns retrieval-corpus curation and lifecycle management including source selection, freshness, versioning, and retirement of stale knowledge
Builds grounding evaluation, regression testing, and quality metrics for retrieval-augmented features
Context Quality, Evaluation & Enablement
Defines and runs evaluation for context quality and grounding fidelity across products
Contributes context scaffolding and standards for the Advantage Forge citizen-engineer program and product teams
Mentors junior and mid-level engineers on software engineering and context-engineering practices and maintains team documentation
Risk Management
Follows firm security, privacy, and compliance standards (SOC 2, PCAOB AS 2201, QC 1000, ISO 27001) when building application code and context assets
Ensures sensitive or restricted data does not enter prompts, corpora, or model context, and maintains traceability of grounded knowledge
Performs other duties as assigned
Willingly accepts share of less desirable assignments
Supervisory Responsibilities
Provides technical leadership and mentorship to engineering and development team members, as assigned
Reviews and provides feedback on the work of other engineers, 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
Five (5) or more years of hands-on professional software development experience building, testing, and deploying production applications or services in C#/.NET or Python, required
Three (3) or more years of experience building LLM-powered features – retrieval-augmented generation (RAG), agent tooling, or prompt and context systems – that reached production users, required
Experience leading the design of at least one production service or application end to end, from requirements through deployment and operation in Microsoft Azure, required
Experience defining reusable engineering assets, patterns, or technical standards adopted by other engineers, required
Experience mentoring or providing technical guidance to other engineers, preferred
Experience delivering AI or knowledge solutions in professional services, assurance, or accounting industries, preferred
License/Certifications
Microsoft Certified: Azure AI Engineer Associate, or equivalent, preferred
Technology
Proficiency in C#/.NET or Python, including automated testing, dependency management, and performance debugging, required
Strong knowledge of Git-based workflows, pull-request review, and CI/CD pipelines (Azure DevOps or GitHub Actions), required
Strong 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 (embeddings, hybrid search, re-ranking, indexing), required
Experience with agent-instruction and skill systems (CLAUDE.md and equivalents), agentic development tooling (Claude Code or equivalent), and Markdown-based knowledge structures, required
Experience with Azure Container Apps, Semantic Kernel, and Dapr, preferred
Knowledge, Skills, & Abilities
Ability to design, build, debug, and ship working software independently and to explain technical decisions clearly in a live, hands-on setting
Deep knowledge of context engineering, retrieval design, and grounding for production LLM systems
Ability to work independently on complex, ambiguous problems and provide technical guidance to others
Ability to translate subject-matter expertise into machine-consumable knowledge
Excellent problem-solving skills with a focus on accuracy, reliability, and grounding quality
Strong communication skills with the ability to convey technical concepts to technical and non-technical audiences
Self-directed with the ability to manage multiple priorities and competing deadlines
Collaborative mindset with the ability to build relationships across teams
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: $110,000 - $140,000
- Maryland Range: $110,000 - $140,000
- NYC/Long Island/Westchester Range: $110,000 - $140,000
