AI/ML Engineer - Agentic AI & Vertex AI at Capgemini
Atlanta, GA
$53,580–$122,400from the description
Aug 3, 2026
Atlanta, GA
Sep 24, 2026
What this job asks for AI summary
This role focuses on designing and building agentic AI systems and production ML pipelines on Google Cloud, primarily using Vertex AI and BigQuery. The engineer will architect multi-agent workflows with tools like Vertex AI Agent Builder and ADK, build real-time and batch data pipelines, and implement retrieval-augmented architectures. It suits an experienced ML/AI practitioner with strong GCP and Python skills who can translate enterprise business requirements into scalable, cloud-native AI solutions.
Senior level · Atlanta-Sandy Springs-Alpharetta, GA · Full-time
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
Roughly 8 people in the Atlanta-Sandy Springs-Alpharetta, GA area plausibly meet what this posting asks for (data scientists). range 2–10
Applicant volume Light — Few people clear these requirements, so an application that does clear them gets looked at. Worth applying to even if you miss a nice-to-have.
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 $111,336 (middle half $92,500–$147,851). 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 Aug 6, 2026. It is a model, not a headcount.
Why we read it this way (6)
The compensation range ($53,580–$122,400/year) is unusually wide and spans Junior through Senior levels; the posting explicitly states candidates are rarely hired at the top of the range and that actual pay depends on many factors.
The role is classified as 'Experienced Professionals' in the posting footer, supporting a Senior seniority assessment despite no explicit years-of-experience requirement.
SOC classification is a genuine judgment call: the role blends ML/data science work (model training, fine-tuning, evaluation, RAG) with substantial software engineering (agentic system design, pipeline building, API integration). Data Scientists (15-2051) was chosen as primary given the ML modeling emphasis; Software Developers (15-1252) is a close runner-up given the engineering depth.
AlloyDB and Dataflow appear in the responsibilities/requirements narrative rather than a dedicated 'Required' section header, but are described as core implementation tools for the role's primary deliverables.
Preferred qualifications include domain experience in Financial Services/Banking/FinTech/Retail, PII protection practices (data masking, redaction), and data governance/compliance — these are listed under 'Preferred Qualifications' and are not hard gates.
Ignored 2 non-technology phrase(s) as skills (responsibilities/concepts, not named tools): Vertex AI Agent Builder, Vertex AI Model Garden.
Read the full posting
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