AI Engineer and Architect

Guidehouse•Tysons, VA

About The Position

The AI Engineer supports system-specific implementations that embed AI/ML and agentic automation to accelerate metadata identification, extraction, enrichment, and documentation. This role owns the metadata harvesting plan and leads the design and implementation of AI-assisted workflows that generate validated metadata artifacts (e.g., semantic tags, data dictionary entries, and supporting documentation) using client-approved tools and human-in-the-loop steward review cycles. The AI Engineer is accountable for automated metadata harvesting and documentation, delivering automated harvesting and AI-assisted documentation outputs while operating within approved governance and security guardrails.

Requirements

Must be able to OBTAIN and MAINTAIN a Federal or DoD "PUBLIC TRUST". Candidates with an ACTIVE SECRET CLEARANCE OR PUBLIC TRUST or SUITABILITY are preferred. Once onboard with Guidehouse, new hire MUST be able to OBTAIN and MAINTAIN a Federal or DoD "SECRET" security clearance.
Bachelor’s degree obtained.
3-5+ years of experience in AI engineering, applied ML, or data/AI solution development in enterprise environments (senior-level expectations aligned to the referenced agentic AI role).
Strong proficiency in Python; working proficiency in SQL; familiarity with R is beneficial for statistical validation/profiling.
Hands-on experience with Generative AI (LLMs and/or similar) and Agentic AI architectures applied to real workflows.
Experience with RAG pipelines, and familiarity with vector databases and knowledge graphs (for semantic retrieval and metadata-driven discovery).
Experience with AI/ML frameworks such as TensorFlow and/or PyTorch (or equivalent).
Demonstrated ability to translate AI techniques into practical, auditable engineering outcomes and communicate clearly with both technical SMEs and non-technical stakeholders.

Nice To Haves

Familiarity with AI governance, ethical/responsible AI practices, and operating in compliance-driven environments.
Bachelor’s degree in Computer Science, Information Systems, Engineering, Data Science, or related field
Proficiency with visualization tools and interactive dashboards to communicate metadata quality, coverage, and validation results.
Agile delivery experience (sprint-based delivery; backlog/issue tracking).
Prior consulting experience delivering AI-enabled solutions in complex enterprise environments.
Exposure to metadata/catalog patterns and metadata-focused engineering (harvesting, catalog population concepts, semantic discovery / lineage concepts).

Responsibilities

Design AI-assisted metadata harvesting & enrichment: Build AI/ML-enabled approaches to identify, extract, normalize, and enrich technical, business, and operational metadata from structured and semi-structured sources (e.g., databases, pipelines, file/document repositories) and generate semantic tags and documentation outputs.
Implement agentic/GenAI workflows for metadata documentation: Design and implement agentic AI patterns to support autonomous or semi-autonomous metadata exploration, summarization, and documentation generation—while maintaining human validation and auditability.
Own the metadata harvesting plan: Define scope, sequencing, cadence, source coverage, extraction methods, staging, validation, and handoffs; maintain decision logs and traceability for stewardship and governance review.
Human-in-the-loop steward validation: Facilitate structured in-person/virtual review cycles with data stewards to validate AI-generated metadata, resolve discrepancies, and continuously improve extraction/enrichment accuracy.
Responsible automation scoping: Identify where automation is feasible vs. where systems require manual curation; document constraints and remediation needs without attempting to “automate through” non-harvestable environments.
Build and maintain scalable pipelines (metadata-focused): Implement and maintain scalable pipelines that integrate structured and unstructured sources for metadata extraction and enrichment; apply strong engineering discipline for reliability and repeatability.
RAG / knowledge integration for semantic discovery: Lead work that connects harvested metadata to semantic search patterns using retrieval-augmented generation (RAG) concepts and, where applicable, knowledge graph integration to improve discoverability and semantic alignment.
Metadata quality metrics & validation controls: Define and implement checks for completeness, accuracy, timeliness, and consistency; flag issues for remediation and support governance escalation where required.
Technical testing & verification: Plan and execute tests to verify the metadata extraction/enrichment process works reliably across supported sources; contribute to regression coverage and repeatable validation workflows.
Mentor and elevate engineering rigor: Mentor junior team members on agentic AI/GenAI engineering patterns, metadata quality practices, and documentation standards.

Benefits

Medical, Rx, Dental & Vision Insurance
Personal and Family Sick Time & Company Paid Holidays
Position may be eligible for a discretionary variable incentive bonus
Parental Leave and Adoption Assistance
401(k) Retirement Plan
Basic Life & Supplemental Life
Health Savings Account, Dental/Vision & Dependent Care Flexible Spending Accounts
Short-Term & Long-Term Disability
Student Loan PayDown
Tuition Reimbursement, Personal Development & Learning Opportunities
Skills Development & Certifications
Employee Referral Program
Corporate Sponsored Events & Community Outreach
Emergency Back-Up Childcare Program
Mobility Stipend