Senior AI Enablement Software Engineer

$128K - $192K Overland Park, KS, US Senior AI Software Engineer

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Skills & Technologies

PythonTypescript

About This Role

AI job market dashboard showing open roles by category

Aechelon Technology, a Shield AI company, is a leading producer of 3D simulator content, including Geospecific visual/sensor databases and realistic 3D models. We seek people who share our passion for real-time computer graphics and commitment to our mission of helping make our Nation's pilots safer. We will give you a chance to work with some of the most talented people in the graphics industry.

The Senior AI Enablement Software Engineer will design tools and infrastructure that help teams use artificial intelligence effectively. This hands-on individual contributor will work with locally hosted models, build reusable AI skills and integrations, and introduce practical AI workflows.

The engineer will initially focus on enabling software development teams, with the opportunity to expand these capabilities across the organization. They will collaborate with infrastructure, security, and technical peers while mentoring others on the effective and responsible use of AI.

Responsibilities will include, but not be limited to the following:

  • Design and implement AI tools, services, reusable skills, agent tools, and developer infrastructure.
  • Evaluate, deploy, and optimize locally hosted models on individual workstations and shared internal systems.
  • Connect models with source repositories, documentation, build systems, issue trackers, and other internal tools.
  • Build workflows and curate approved datasets for training and fine-tuning models using internal source code and technical documentation.
  • Work with software teams to identify practical use cases and turn successful prototypes into supported capabilities.
  • Develop and maintain practices for model configuration, skill development, prompt management, evaluation, and versioning.
  • Assess model and workflow quality, performance, resource usage, reliability, and suitability for their intended purpose.
  • Collaborate with infrastructure and security teams on deployment, data handling, access control, governance, and handoff for operational support.
  • Diagnose issues involving model behavior, context management, tool integration, inference performance, and hardware utilization.
  • Create documentation, examples, and training materials, and mentor users on appropriate AI practices, limitations, and review processes.
  • Track developments in local models, agent systems, inference runtimes, and AI-assisted development tools.

Minimum Qualifications:

  • Bachelor's degree in Engineering, Computer Science, Data Science, or a similar technical field, or equivalent professional experience.
  • Significant experience developing production software, developer tools, libraries, services, APIs, or platform infrastructure for use by other teams.
  • Experience working with or integrating into software codebases written in Python and C++.
  • Experience integrating generative AI into applications and workflows, including model inference, training or fine-tuning, prompt design, tool calling, structured output, and retrieval-augmented generation.
  • Experience running or integrating models in local or privately hosted environments.
  • Understanding of dataset preparation, model evaluation, context management, and the limitations of AI-generated output.
  • Familiarity with Windows and Linux development environments.
  • Strong analytical, troubleshooting, communication, and collaboration skills.
  • Ability to explain AI capabilities and limitations to audiences with different levels of technical experience.

Preferred Qualifications:

  • Experience training or adapting Nemotron or comparable open-weight models using source code and technical documentation.
  • Experience creating Codex-style skills, agent tools, plugins, or Model Context Protocol services.
  • Experience with GPU computing, model quantization, or inference optimization.
  • Experience scripting or developing tools in TypeScript.
  • Familiarity with AI security, data governance, access control, and responsible-use practices.
  • Familiarity with real-time graphics, geospatial visualization, content creation, or other technical domains supported by the organization.
  • Experience with Git or Perforce and defect-tracking systems such as Jira or Bugzilla.

Compensation and Benefits

$128,000 - $192,000 per year

Final compensation is based on factors such as the candidate's skills, qualifications, and experience.

We offer a very attractive compensation package including competitive base salary, company performance-based profit sharing, 401k, 100% employer paid health benefits (medical, dental, vision, life, std, ltd, and life insurance plans).

No relocation reimbursement provided.

This position description is not intended to be a complete listing of activities, duties or responsibilities that are required of the employee holding this position. Duties, responsibilities and activities may be changed or others may be assigned at any time by the Company with notice to the employee.

Shield AI is an equal opportunity employer. We are committed to providing access and opportunities to individuals with disabilities. If you are an applicant who is unable to fully utilize/access our application process because of a disability, Aechelon Technology will provide a reasonable accommodation. Please send an email to [email protected] to request that accommodation, and please be sure to include a detailed description of your requested accommodation, your name and preferred method of contact.

Salary Context

This $128K-$192K range is in the lower quartile for AI Software Engineer roles in our dataset (median: $194K across 269 roles with salary data).

Role Details

Title Senior AI Enablement Software Engineer
Location Overland Park, KS, US
Category AI Software Engineer
Experience Senior
Salary $128K - $192K
Remote No

About This Role

AI Software Engineers build the applications and systems that AI models run inside. They own the API layers, data pipelines, frontend integrations, and infrastructure that turn a model into a product users interact with. Every AI company needs engineers who can build the software around the AI.

The challenge is building reliable systems around inherently unreliable components. Models are probabilistic. They'll give different answers to the same question. They hallucinate. They're slow. They're expensive. Your job is to build an application layer that handles all of this gracefully while delivering a product that users trust and enjoy.

Across the 11,520 AI roles we're tracking, AI Software Engineer positions make up 5% of the market. At Aechelon Technology Inc., this role fits into their broader AI and engineering organization.

AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.

What the Work Looks Like

A typical week includes: building API endpoints that serve model inference with caching and fallback logic, designing the data pipeline that feeds context to a RAG system, implementing streaming responses in the frontend, debugging a race condition in the async inference pipeline, and optimizing database queries for the vector search layer. It's full-stack engineering with AI at the center.

AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.

Skills Required

Python (20% of roles) Typescript (3% of roles)

Full-stack engineering skills with AI integration experience. Python and TypeScript are the most common requirements. You'll need to understand API design, database architecture, and how to build reliable systems around probabilistic outputs. Experience with streaming, async processing, and caching patterns is increasingly important as real-time AI applications proliferate.

Knowledge of vector databases, embedding APIs, and LLM integration patterns (function calling, structured outputs, retry logic) differentiates AI software engineers from general software engineers. Understanding cost optimization (caching strategies, model routing, batched inference) is valuable since inference costs can dominate application economics.

Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.

Compensation Benchmarks

AI Software Engineer roles pay a median of $220,000 based on 1,688 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($160K) sits 27% below the category median. Disclosed range: $128K to $192K.

Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.

Aechelon Technology Inc. AI Hiring

Aechelon Technology Inc. has 1 open AI role right now. They're hiring across AI Software Engineer. Based in Overland Park, KS, US. Compensation range: $192K - $192K.

Location Context

Across all AI roles, 6% (708 positions) offer remote work, while 10,737 require on-site attendance. Top AI hiring metros: New York (3,257 roles, $221,000 median); San Francisco (2,766 roles, $260,550 median); Los Angeles (1,580 roles, $210,000 median).

Career Path

Common paths into AI Software Engineer roles include Software Engineer, Full-Stack Developer, Backend Engineer.

From here, career progression typically leads toward Staff Engineer, AI Architect, Engineering Manager.

If you're a software engineer, you're already 80% there. Learn the AI integration patterns: RAG, streaming inference, function calling, structured outputs. Build a project that demonstrates you can wrap an AI model in a production-quality application with proper error handling, caching, and user experience. That's the portfolio piece that gets you hired.

What to Expect in Interviews

Technical screens look like standard software engineering interviews with an AI twist. Expect system design questions about building reliable applications around probabilistic models: handling streaming responses, implementing retry logic for API failures, and designing caching strategies for LLM outputs. Coding rounds test standard algorithms plus practical integration patterns like async processing and rate limiting.

When evaluating opportunities: Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.

AI Hiring Overview

The AI job market has 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.

The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 roles).

AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.

The AI Job Market Today

The AI job market spans 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.

The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (140) are outnumbered by mid-level (5,630) and senior (4,176) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,574 positions, representing the bottleneck between technical execution and organizational strategy.

Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.

AI compensation is structured in clear tiers. The market median sits at $213,480. Top-quartile roles start at $264,000, and the 90th percentile reaches $318,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.

Category matters for compensation. AI Safety roles lead at $266,050 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.

The most in-demand skills across all AI postings: Python (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.

Frequently Asked Questions

Based on 1,688 roles with disclosed compensation, the median salary for AI Software Engineer positions is $220,000. Actual compensation varies by seniority, location, and company stage.
Full-stack engineering skills with AI integration experience. Python and TypeScript are the most common requirements. You'll need to understand API design, database architecture, and how to build reliable systems around probabilistic outputs. Experience with streaming, async processing, and caching patterns is increasingly important as real-time AI applications proliferate.
About 6% of the 11,520 AI roles we track offer remote work. Remote availability varies by company and seniority level, with senior and leadership roles more likely to offer location flexibility.
Aechelon Technology Inc. is among the companies actively hiring for AI and ML talent. Check our company profiles for detailed breakdowns of open roles, salary ranges, and hiring trends.
Common next steps from AI Software Engineer positions include Staff Engineer, AI Architect, Engineering Manager. Progression depends on whether you lean toward technical depth, people management, or product strategy.

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