Austin, TX

Salary
$115,003–$144,997
Posted
Jul 19, 2026
Location
Austin, TX
Last confirmed open
Jul 21, 2026

What this job asks for AI summary

A hands-on software engineering role within a financial firm's enterprise architecture group, focused on building and maintaining platforms that help internal development teams adopt AI responsibly at scale. Day-to-day work spans agentic frameworks, governance tooling, architecture assessment systems, and cloud-native services. Suited to engineers with 4+ years of experience and practical exposure to generative AI or LLM-based development.

Mid level · 4+ years · National · Full-time

Must have (6)
agentic systemsRAGLLMsRESTrelational databasesNoSQL
Nice to have (6)
vector databasesGCP, AWS or Azuremodel evaluationembeddingsMCPagile

“or” means any one of them counts — you don't need all of them.

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

What gives you an edge
RAG8%

Rare in this occupation — lead with these, and say what you built with them.

What won't set you apart
relational databases51%

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).

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 (7)

The title is 'Software Developer' with no level modifier, so advertised seniority is Unspecified; the 4+ years requirement and scope (contributing to a portfolio, partnering with architects) support a Mid-level classification.

The degree requirement is 'Bachelor's degree in Computer Science or equivalent professional experience' — equivalent experience is explicitly accepted, so the degree requirement is set to None.

The AI/LLM hands-on requirement is framed as 'at least one of the following areas' (agentic systems, RAG, prompt engineering, generative AI, LLM applications) — all are captured as required since the gate is on the capability cluster, not any single named tool.

Cloud platform experience (GCP, AWS, Azure) appears only under Preferred Qualifications; GCP is listed as the primary with AWS and Azure as alternatives since all three are named interchangeably.

Vector databases, embeddings, and MCP appear under Preferred Qualifications ('Exposure to agent orchestration frameworks, model context protocols, vector databases, embeddings…') and are marked preferred accordingly.

No compensation figures are stated; the posting only mentions eligibility for bonus/incentive in addition to salary range, but no range is provided.

Ignored 3 non-technology phrase(s) as skills (responsibilities/concepts, not named tools): object-oriented design, cloud-native development, AI governance.

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