AI Engineer - Nexus Black

IFS•Colorado Springs, CO

2d•$90,000 - $100,000•Hybrid

About The Position

We are seeking an AI Engineer with a builder’s mindset to bring cutting-edge AI agents and workflows into production for IFS Nexus Black. You’ll turn ambiguous customer problems into working prototypes in days/weeks, iterate in the field, and scale the winners into reliable products. You will work across the stack—prompting, tools, retrieval, evaluation, guardrails, and backend services—collaborating closely with product, design, and data engineering. Expect high autonomy, direct exposure to customers, and a fast loop from idea → demo → production.

Requirements

4–8+ years as a software/AI engineer, including shipping 0→1 products or features in startup-like environments.
Strong in Python (and comfortable with TypeScript); able to build production services and integrate with external systems/APIs.
Proven experience taking LLM/agent systems to production (tool use, function calling, planning/exec, retrieval-augmented generation).
Hands-on with evaluation (offline/online), prompt engineering, tracing, and observability for AI systems.
Familiarity with vector databases/search, embeddings, and context/retrieval patterns; pragmatism about when/what to index.
Solid backend fundamentals (services, queues, data models, CI/CD, cloud) and a bias to automate what you repeat.
Excellent communication and product sense—able to turn fuzzy business problems into clear technical plans and measurable outcomes.
Fast mover: comfort with ambiguity, crisp prioritization, and the judgment to ship small, safe increments quickly.

Nice To Haves

open-source contributions, hackathon wins, or a portfolio of shipped side projects; experience at both an early-stage startup and a larger-scale system.

Responsibilities

Rapidly prototype AI agents and guided workflows that solve concrete customer problems; ship thin slices, instrument, and iterate with real usage.
Design and implement cloud-native backend services that orchestrate prompts, tools, retrieval, and automations; own the path from POC to production.
Build robust retrieval for LLMs (search/vector pipelines, context assembly, tool selection) in partnership with data engineering.
Establish evaluation loops: offline/online tests, golden sets, regression checks, and human-in-the-loop review to measurably improve quality and safety.
Contribute to developer experience: reusable agents/tools, SDKs, templates, and internal documentation that speed up future builds.
Collaborate with customers and internal stakeholders to frame problems, define success metrics, and translate insights into productizable solutions.
Lead design discussions and code reviews; mentor teammates and raise the quality bar while keeping momentum high.