HR Process Automation & AI Specialist

Bechtel•Glendale, AZ

98d•Hybrid

About The Position

The HR Process Automation & AI Specialist accelerates productivity, quality, and data driven decision‑making across HR by embedding Artificial Intelligence (AI), automation, and advanced analytics into core workflows. Partnering with HR leaders, process owners, EPC functions, various GBUs, and I&D technology teams, this role identifies high‑value opportunities, designs scalable and secure AI solutions, and stewards end‑to‑end delivery—from discovery and proof‑of‑concept through machine learning operations (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. This position is designated as part-time telework per our global telework policy and will require at least three days of in-person attendance per week at the assigned office or project (Reston, VA or Glendale, AZ). Weekly in-person schedules will be determined by the individual and their supervisor, in consultation with functional or project leadership. #LI-KL1

Requirements

Bachelor’s degree in a technical, analytical, or business‑related field (or international equivalent) and 10–13 years of relevant experience; or 14–17 years of relevant work experience. Advanced degree in Data Science/Artificial Intelligence/Machine Learning/Computer Science a plus.
Demonstrated delivery in AI, automation, data/descriptive and predictive analytics, digital transformation, and process automation roles combining technical depth with measurable business impact.
Experience leading cross‑functional, multi‑stakeholder initiatives in complex, global environments.
Solid foundation in AI/ML concepts: supervised/unsupervised learning, GenAI/LLMs, natural language processing (NLP), embeddings, vector search, and RAG architectures.
Prompt engineering and evaluation (prompt libraries, guardrails, safety filters); understanding of fine‑tuning vs. grounding trade‑offs and alignment techniques.
Model evaluation & monitoring: offline/online metrics, A/B testing, drift detection, bias/fairness testing, and retraining strategies.
Experience with MLOps (experiment tracking, model registry, CI/CD, feature stores, reproducibility) and data orchestration (e.g., Azure ML, Databricks, MLflow; pipelines via GitHub Actions/Azure DevOps).
Strong data fundamentals: data quality management, feature engineering, labeling/annotation strategies, secure data access, and privacy‑preserving techniques.
Familiarity with modern cloud and SaaS platforms (e.g., Azure data & AI services), Power Platform/Power Automate, enterprise APIs, and HR systems (SAP SuccessFactors, UKG, ServiceNow HRSD).
Proficiency in process improvement methodologies applied to digital/AI‑enabled workflows; excellent communication across technical and non‑technical audiences.
Strong facilitation and influence skills; ability to drive change management and adoption within HR.
Working knowledge of enterprise data governance, privacy, security, and ethical AI; ability to create comprehensive documentation (design decisions, model cards, runbooks) to ensure auditability.

Nice To Haves

Experience building HR‑specific AI (e.g., candidate screening assistants grounded in policy, knowledge bots for HR services, attrition/mobility analytics) with bias mitigation and explainability.
Hands‑on with vector databases and enterprise search (e.g., Azure AI Search), content moderation, and policy enforcement pipelines.
Certifications in Azure AI/ML, Data Engineering, or Responsible AI.

Responsibilities

Lead structured discovery to identify, assess, and prioritize HR automation and AI use cases aligned to enterprise productivity goals and priorities; 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 (LLM) with retrieval augmented generation (RAG), fine‑tuning vs. grounding) for each use case.
Define success metrics, telemetry, and guardrails at the outset (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 the business.
Implement LLM solutions with strong prompt engineering, chain‑of‑thought alternatives (where appropriate), 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, MLflow/Databricks, and GitHub 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 use cases (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 (latency, resiliency) while meeting quality targets.
Track outcome metrics (cycle time reduction, accuracy lift, case deflection, employee/internal customer satisfaction, compliance findings) and publish value realization/return on investment reports/metrics.