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About This Role
Keyhole Software is hiring Senior Agentic AI Software Engineers with strong software engineering fundamentals and hands-on experience building agentic systems to support a long-term enterprise healthcare client engagement. This consulting role is ideal for experienced engineers who understand how to move beyond basic LLM integrations and build reliable, maintainable agent workflows and supporting infrastructure.
- Title: Senior Agentic AI Software Engineer
- Location: Remote within the United States. Preference will be given to candidates located in the Kansas City metro area, followed by candidates residing elsewhere in Kansas or in Nebraska, Texas, Florida, or Arizona.
- Eligibility: Candidates must be authorized to work and reside in the United States. No Corp-to-Corp (C2C) or Contract-to-Hire (C2H). At this time, Keyhole Software is unable to provide visa sponsorship.
About the Role
Keyhole Software is seeking experienced Senior Agentic AI Software Engineers to join our team. Our consultants are full-time members of Keyhole Software, working across a variety of long-term client engagements throughout their careers.
For this engagement, you’ll help build DataAssist, a purpose-built agentic harness designed to assist data engineers with tasks such as data extraction and code generation. The solution will integrate with the client’s existing model gateway and agent infrastructure on Google Cloud Platform.
The technology stack is evolving and generally includes Python, Go, Google Cloud Platform, and Vertex AI, along with specialized tools selected as the architecture develops. We’re looking for adaptable engineers who can work effectively in an emerging technical space, evaluate options thoughtfully, and build reliable systems without depending on a single framework or fixed implementation pattern.
Strong fundamentals and meaningful agentic engineering experience are essential. You should understand the engineering challenges involved in agent orchestration, tool use, context management, structured outputs, evaluation, observability, reliability, and safeguards. Previous experience building an agent harness or working on a data or data-platform engineering team would be especially valuable.
If you enjoy building practical AI systems, working through evolving technical challenges, and creating tools that make other engineers more effective, we’d love to talk with you.
Your Responsibilities
- Design, build, test, and refine components of a purpose-built agentic harness for data engineering workflows.
- Develop production-quality services and agent workflows using Python, Go, and related technologies.
- Integrate the harness with an existing model gateway and agent infrastructure on Google Cloud Platform.
- Build and maintain agent capabilities involving tool use, structured outputs, task orchestration, context management, and code generation.
- Develop integrations that allow agents to safely interact with data systems, engineering tools, APIs, and internal services.
- Improve the reliability, observability, testability, and performance of agentic workflows.
- Design evaluations and testing strategies for non-deterministic AI behavior, failure modes, and generated outputs.
- Troubleshoot issues across application code, model interactions, tools, prompts, cloud services, and supporting infrastructure.
- Apply appropriate safeguards, access controls, and engineering judgment when agents interact with sensitive systems or data.
- Collaborate with client engineers, data engineers, and technical stakeholders to understand workflows and translate them into useful agent capabilities.
- Evaluate emerging libraries, frameworks, and implementation approaches as the technology stack evolves.
- Write clean, maintainable, well-tested code and participate in collaborative GitHub-based development workflows.
- Create clear documentation for architecture, agent behavior, integrations, operational processes, and technical decisions.
- Work independently, manage priorities effectively, and become productive within an established client environment quickly.
- Contribute to a collaborative Keyhole consulting culture by sharing knowledge and supporting fellow consultants.
Required Qualifications
- 7+ years of professional software engineering experience.
- Hands-on experience designing and building agentic AI systems beyond basic prompt engineering or chatbot integrations.
- Strong software engineering fundamentals, including system design, APIs, automated testing, debugging, source control, and maintainable code.
- Production development experience with Python or Go, with the ability to work effectively across an evolving technology stack.
- Experience developing applications or services that integrate with large language models and model APIs.
- Understanding of agentic engineering concepts such as tool calling, orchestration, context management, structured outputs, state, retries, failure handling, and safeguards.
- Experience evaluating, testing, and troubleshooting LLM-powered or other non-deterministic systems.
- Experience integrating multiple services, APIs, tools, or data sources into a cohesive application or platform.
- Proficiency with Git and collaborative development workflows, including pull requests and code reviews.
- Demonstrated ability to make sound engineering decisions in an emerging technical area where tools and patterns continue to evolve.
- Strong analytical and problem-solving skills with the ability to troubleshoot issues across application, model, integration, and infrastructure layers.
- Excellent communication skills and the ability to work directly with technical and non-technical stakeholders.
- Comfortable taking direction from a client team, working independently, and adapting to changing priorities.
- Demonstrated experience using AI-assisted development tools such as GitHub Copilot, ChatGPT, Claude, Cursor, or similar as part of everyday technical work.
Preferred Qualifications
- Experience designing or building an agent harness, agent runtime, agent platform, or comparable orchestration framework.
- Production experience with both Python and Go.
- Experience with Google Cloud Platform and Vertex AI.
- Experience integrating with centralized model gateways or enterprise AI platforms.
- Experience building agents that perform data engineering, data extraction, code generation, or developer-tooling tasks.
- Previous experience working on a data engineering or data-platform team.
- Experience with agent evaluation frameworks, tracing, observability, or quality measurement.
- Experience with retrieval-augmented generation, vector search, or grounding techniques.
- Experience implementing security controls and safeguards for AI systems that access sensitive data or execute tools.
- Experience working with healthcare data or in a healthcare enterprise environment.
- Experience working as a consultant or in client-facing technical roles.
- Relevant Google Cloud or machine learning certifications.
Why Join Keyhole Software?
At Keyhole Software, you’ll enjoy the best of both worlds: the stability of full-time employment and the variety of working on diverse, long-term client projects.
- Dynamic Opportunities: Work on challenging enterprise projects that keep your technical skills sharp while offering variety across industries and technologies.
- Collaboration and Support: Join a team of experienced developers, architects, data engineers, and consultants who are available to collaborate and share expertise across engagements.
- Long-Term Growth: Develop your career with opportunities to learn new technologies, expand your consulting skills, and work across multiple client engagements.
- Knowledge-Sharing Culture: Be part of a team that values knowledge transfer, technical education, and leaving client teams stronger and more self-sufficient.
- Comprehensive Benefits: Enjoy competitive pay, health insurance options, a 401(k) plan, and more.
At Keyhole Software, you’re not just taking on a project. You’re joining a team that’s committed to your success, development, and long-term satisfaction.
What We Offer
Competitive Compensation: Compensation based on experience and qualifications
Comprehensive Benefits Package:
- Medical insurance (traditional and HDHP options)
- Dental and vision insurance
- FSA and dependent care options
- Life and disability insurance
- 401(k) with discretionary employer contributions
- Supplemental insurance options
Collaborative Team Environment: Access to a highly experienced consulting network for ongoing growth and support.
Apply
To apply, please send a personalized email to [email protected], including:
- A copy of your resume
- Your current city and state
- A brief summary of your qualifications for this role
Candidates must be authorized to work and reside in the United States. This position is remote, with preference given to candidates in the Kansas City metro area, followed by candidates residing elsewhere in Kansas, Missouri, or in Nebraska, Texas, Florida, or Arizona. We do not engage candidates on a 1099 or Corp-to-Corp (C2C) basis. To ensure an authentic and meaningful hiring process, all interviews are conducted live, either in person or by video, and we take extra care to verify identities, backgrounds, and work histories.
Role Details
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 8,943 AI roles we're tracking, AI Software Engineer positions make up 4% of the market. At Keyhole Software, 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
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 1,448 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
Keyhole Software AI Hiring
Keyhole Software has 1 open AI role right now. They're hiring across AI Software Engineer. Based in Lenexa, KS, US.
Location Context
Across all AI roles, 6% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 median); Los Angeles (1,340 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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
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