HR Process Automation & AI Specialist

Requisition : 292365

• Relocation Authorized: None
• Telework Type: Part-Time Telework
• Work Location: Reston, VA;
  Glendale, AZ

Extreme success in building global infrastructure has driven Bechtel’s growth since 1898. Our teams deliver more than 25,000 projects across 160 countries, creating jobs, strengthening economies, protecting energy, and enhancing global resilience.

With a commitment to quality, people, and relentless delivery, we align our capabilities with customer objectives to achieve lasting impact. Bechtel serves the Infrastructure, Nuclear, Security & Environmental, Energy, Mining & Metals, and Manufacturing & Technology markets, spanning initial planning to operations.

The HR Process Automation & AI Specialist accelerates productivity, quality and data‑driven decision‑making across HR by embedding AI, automation and analytics into core workflows. Partnering with HR leaders, process owners and technology teams, the specialist identifies high‑value opportunities, designs scalable, secure AI solutions and stewards end‑to‑end delivery—from discovery and proof‑of‑concept through MLOps, productionization, monitoring and continuous improvement. Operating at the intersection of process improvement, AI strategy, data engineering and responsible AI, the specialist ensures solutions are explainable, auditable and trusted, improving accuracy, speed, compliance and employee experience.

Position is part‑time telework. Requires at least three days in‑person attendance per week at the assigned office or project (Reston, VA or Glendale, AZ). Weekly in‑person schedules are determined in consultation with supervisor and leadership.

• Lead structured discovery to identify, assess and prioritize HR automation and AI use‑cases aligned with enterprise productivity goals; quantify value and risk.
• Translate HR process needs into clear solution designs (process maps, data flows, model design choices), selecting the right patterns (automation, machine learning, large language models with retrieval‑augmented generation, fine‑tuning vs. grounding) for each use‑case.
• Define success metrics, telemetry and guardrails (accuracy, bias/fairness, latency, cost, adoption, compliance).
• Lead full‑lifecycle delivery: feasibility, proof‑of‑concept, pilot, production rollout and scale‑out – coordinating scope, schedule, resources and change management across HR, IS&T and business.
• Implement LLM solutions with strong prompt engineering, chain‑of‑thought alternatives, RAG using governed HR data and hallucination mitigation techniques; optimize for latency and cost.
• Collaborate with data architects/engineers/AI specialists to ingest and govern HR data (from HRIS/ATS/LMS), build feature pipelines and enable secure access patterns (e.g., attribute‑based access) for AI applications.
• Establish and maintain CI/CD for ML/AI (experiment tracking, model registry, reproducible training), including automation via tools such as Azure ML, Databricks, MLflow, and Git Hub Actions.
• Define model lifecycle standards (versioning, promotion criteria, rollback, retraining schedules) and automate data and concept drift detection with alerting and SLA/SLO reporting.
• Implement observability (dashboards for quality, latency, cost, safety events) and incident response runbooks for AI services.
• Ensure adherence to data privacy, security and ethical AI principles; operationalize bias testing, disparate‑impact assessment, red‑teaming, content moderation/guardrails and human‑in‑the‑loop controls.
• Partner with Security, Legal and Compliance to maintain audit trails, model documentation (model cards, datasheets) and evidence for regulatory or customer audits.
• Design AI/automation solutions for HR functions (talent acquisition, internal mobility, pay/benefits queries, policy Q&A, case management, knowledge management, workforce planning), with special care for fairness in decisions impacting people.
• Drive adoption and enablement: build AI literacy materials, conduct training, create usage playbooks and support change champions within HR.
• Optimize cost‑to‑serve (token/compute utilization, throughput, caching, content filters) and performance…