AI / ML Engineer
Actively Hiring
Full-time Posted 7 months ago
Responsibilities
- check_circle Serves as the lead AI/ML engineer responsible for developing, optimizing, and operationalizing advanced LLM-driven workflows for the FBI adjudication platform. Drives design and implementation of inference pipelines, RAG workflows, retrieval systems, prompt architectures, and model lifecycle processes.
- check_circle Leads development of dual-path model operations supporting self-hosted open‑weight LLMs in AWS GovCloud and FedRAMP‑High managed endpoints. Engineers GPU-based inference infrastructure, model containerization, distributed inference strategies, and performance‑optimized reasoning workflows.
- check_circle Designs and maintains continuous learning systems including SFT, LoRA/QLoRA adapters, dataset curation, automated evaluation suites, hallucination detection, bias evaluation, and model drift monitoring. Ensures models are safe, accurate, reliable, and aligned to SEAD‑4 adjudication criteria.
- check_circle Ensures all model operations adhere to FedRAMP High, RMF, CJIS, and FBI ATO requirements, including controls for logging, access, explainability, evidence provenance, and data protection.
- check_circle Develop and maintain LLM inference pipelines supporting long‑document reasoning, multi‑document fusion, entity extraction, anomaly detection, SEAD‑4 scoring, and structured memo generation.
- check_circle Build and manage advanced prompt architectures including system prompts, instruction sets, retrieval‑augmented prompts, multi-step reasoning flows, and output‑schema enforcement to ensure accuracy and stability.
- check_circle Implement distributed GPU inference frameworks (vLLM, TGI, DeepSpeed, Sagemaker) and optimize workloads with KV caching, tensor parallelism, dynamic batching, and memory efficiency strategies.
- check_circle Develop output‑validation routines enforcing schema correctness, key‑evidence referencing, structured scoring, and quality controls for all model‑generated adjudicative content.
- check_circle Implement RAG architectures including embedding generation, vector indexing, long‑context retrieval, and retrieval scoring to support evidence‑grounded outputs for 300–400‑page investigative files.
- check_circle Optimize chunking strategies, ranking models, hybrid search pipelines, and retrieval heuristics to ensure accurate and contextually aligned LLM output.
- check_circle Develop retrieval pipelines that reduce hallucination risk, enforce evidence provenance, and provide structured citation‑linked responses consistent with adjudication standards.
- check_circle Lead development of supervised fine‑tuning (SFT) pipelines using adjudicator examples, SEAD‑4 scoring decisions, historical memos, and SME‑curated datasets.
- check_circle Build LoRA/QLoRA fine‑tuning workflows for secure GovCloud environments, enabling high‑fidelity model specialization without full retraining cycles.
- check_circle Design evaluation suites measuring guideline adherence, evidence alignment, factual consistency, hallucination probability, and reasoning stability across adjudicative categories.
- check_circle Implement model drift detection, scoring distribution monitoring, and automated retraining triggers tied to analyst feedback and dataset evolution.
- check_circle Ensure ML operations align with FedRAMP High and RMF requirements, including encryption, boundary isolation, identity controls, inference logging, and auditable model‑output trails.
- check_circle Establish secure input‑validation flows, restricted‑context enforcement, prompt sanitization, and runtime protections to mitigate security and data‑integrity risks.
- check_circle Develop telemetry pipelines capturing query metadata, retrieval context, response confidence, scoring variances, and override patterns for audit and monitoring.
- check_circle Integrate LLM inference services with backend APIs, scoring engines, memo‑generation modules, entity‑resolution tools, and analyst‑facing UI workflows.
- check_circle Develop supporting microservices for prompt routing, retrieval assembly, evaluation probes, model profiling, and inference orchestration.
- check_circle Collaborate with backend engineers to optimize throughput, latency, concurrency, and reliability for high‑volume adjudication workflows.
- check_circle Work with the AI Solutions Architect to maintain coherence between ML pipelines and system‑wide architecture.
- check_circle Collaborate with adjudicators, SEAD‑4 SMEs, and mission stakeholders to translate adjudicative logic into prompts, features, and structured model outputs.
- check_circle Mentor junior engineers, lead experimentation cycles, participate in design reviews, and contribute to Guidehouse AI/ML engineering best practices.
Basic qualifications
- An ACTIVE and MAINTAINED "TOP SECRET" Federal or DoD security clearance and obtained and maintain TS/SCI clearance.
- Minimum of Eight (8) years of experience in AI/ML engineering with 4+ years focused on NLP, LLMs, or MLOps.
- Bachelor' s Degree or Four (4) additional Years of experience in lieu of degree.
- Expertise in PyTorch, HuggingFace Transformers, vLLM, DeepSpeed, or equivalent frameworks.
- Strong background in retrieval systems, embeddings, RAG pipelines, vector databases, and long‑context optimization.
- Experience implementing MLOps workflows, evaluation frameworks, drift detection, and responsible‑AI safeguards.
- Experience delivering ML systems in secure federal environments subject to FedRAMP High or RMF controls.
- Experience supporting adjudication, continuous vetting, background investigations, or SEAD‑4 scoring workflows.
- Experience deploying open‑weight LLMs in GovCloud or secure enclaves.
- Experience with citation‑grounding pipelines, evidence‑verification workflows, or structured model‑output evaluation.
- AWS Machine Learning Specialty, Solutions Architect Professional, or GPU Compute certifications.
- Experience with explainability tooling, guardrails, reasoning verification, or adversarial evaluation.
Benefits
- check_circle Medical, Rx, Dental & Vision Insurance
- check_circle Personal and Family Sick Time & Company Paid Holidays
- check_circle Parental Leave
- check_circle 401(k) Retirement Plan
- check_circle Group Term Life and Travel Assistance
- check_circle Voluntary Life and AD&D Insurance
- check_circle Health Savings Account, Health Care & Dependent Care Flexible Spending Accounts
- check_circle Transit and Parking Commuter Benefits
- check_circle Short-Term & Long-Term Disability
- check_circle Tuition Reimbursement, Personal Development, Certifications & Learning Opportunities
- check_circle Employee Referral Program
- check_circle Corporate Sponsored Events & Community Outreach
- check_circle Care.com annual membership
- check_circle Employee Assistance Program
- check_circle Supplemental Benefits via Corestream (Critical Care, Hospital Indemnity, Accident Insurance, Legal Assistance and ID theft protection, etc.)
- check_circle Position may be eligible for a discretionary variable incentive bonus
Tags & Focus Areas
Ai Ai Engineer Machine Learning Data Science Generative Ai
About Guidehouse
Job Family:InvestigatorTravel Required:NoneClearance Required:None