AI Engineering Consultant - Utilities
Accenture · Arlington, VA
$54,400–$205,800from the description
Jul 14, 2026
Arlington, VA
Jul 21, 2026
What this job asks for AI summary
This role focuses on building and deploying production-grade AI applications for utilities-sector clients, using large language model providers and major cloud platforms. Day-to-day work involves implementing retrieval-augmented generation pipelines, prompt orchestration, and guardrails, then operationalizing those workloads with containerization, CI/CD, and cloud security practices. It suits engineers with hands-on experience across the full AI delivery lifecycle rather than advisory or research roles.
Mid level · 3+ years · Remote · Full-time
“or” means any one of them counts — you don't need all of them.
Posted 6 times — it's one opening, so apply once.
We read this from the posting text with AI. Skim the description below before ruling yourself out.
How this req sits in the market our data
Rare in this occupation — lead with these, and say what you built with them.
Most people in this occupation already list these. Still required — just not what gets you shortlisted.
What the occupation pays Median $138,970 (middle half $107,524–$175,762). This posting is about at that midpoint.
Estimated from BLS employment for this occupation and area, per-skill prevalence across our listing corpus, and published wage benchmarks — as of Jul 28, 2026. It is a model, not a headcount.
Why we read it this way (11)
The role is posted across multiple US states with no single primary location; the compensation range shown ($54,400–$205,800/yr) spans all listed states (California, New York, Washington, etc.). No single CBSA is determinable.
The JD accepts a Bachelor's degree OR equivalent work experience (minimum 12 years without a degree, or 6 years with an Associate's), so no formal degree is hard-gated.
The 3-year overall experience floor ('minimum of 3 years in software or AI/ML engineering') drives the overall years minimum; the 2-year sub-requirements are captured as the years demanded on individual skills.
OpenAI is listed as the primary name for the cloud/model-provider requirement; Anthropic, AWS Bedrock, Azure OpenAI, and Google Vertex AI are listed as explicit alternatives in the same requirement.
Docker and Kubernetes appear together as a containerization requirement; Docker is named as primary with Kubernetes as an alternative since the JD treats them as a combined 'containerization' gate rather than two separate requirements.
Cloud platform experience (AWS/Azure/GCP) appears throughout the narrative and 'The Work' section but is not explicitly broken out as a standalone minimum requirement — listed as preferred.
Agentic AI and multi-agent orchestration appear only under 'Bonus Points If' and are therefore preferred.
Cloud/AI certifications (AWS, Azure, Google) appear only under 'Bonus Points If' and are omitted as they are credentials, not skills.
A Master's degree is listed under 'Bonus Points If' and is therefore not a hard requirement.
The role requires minimum 1 year of utilities-industry experience (electric, gas, or water), which is a domain/industry gate rather than a named technology skill and so is not captured in the skills list — hiring managers should note this as a hard requirement.
Travel up to 80% is explicitly noted as a possibility depending on client need.
Read the full posting
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