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About This Role
*JOB TITLE:*
AI Software Developer, Senior *CAYUSE COMPANY:*
Cayuse Civil Services, LLC *LOCATION*
Austin, TX *SALARY:*
$116,480\.00\-$166,400\.00 *EMPLOYEE TYPE:*
Full\-Time Salary Exempt
*TRAVEL*
No *RELOCATION*
No Employment in this role is conditional upon successful execution of the contract by the client. The Work
The Senior AI Software Engineer will be responsible for advancing enterprise AI initiatives by extending existing proof‑of‑concept (POC) solutions into scalable, production‑ready web applications that directly support complex engineering workflows. This role focuses on transforming early‑stage AI models and prototypes—such as those supporting plan review automation, asset detection, digital delivery, and other domain‑specific use cases—into fully integrated applications accessible through secure, user‑friendly web interfaces across the organization.
The Senior AI Software Engineer will concentrate on engineering‑related software services, including model ingestion, automated quantity extraction, plan conformance checks, and CI/CD automation. The position requires close collaboration with cross‑functional teams (engineering, product, infrastructure, and security) to ensure solutions are robust, compliant, and aligned with enterprise standards and regulatory requirements.
This position aligns with Cayuse’s core values of Innovation, Excellence, Collaboration, Adaptability, and Integrity by fostering technical solutions that meet customer needs, promoting teamwork, and prioritizing quality in deliverables.
- Design, develop, and maintain production‑grade AI/ML services and web applications that extend existing POC solutions into scalable, secure, and reliable enterprise platforms.
- Implement and optimize AI/ML workflows for:
+ Model ingestion and lifecycle management
+ Automated quantity extraction from plans and documents
+ Plan conformance and rules‑based checks
+ Computer vision–based asset detection and inspection
+ NLP/LLM‑based plan review automation and document analysis
- Build secure, user‑friendly web interfaces and APIs that enable engineering and business users to leverage AI capabilities within their day‑to‑day workflows.
- Architect, implement, and manage CI/CD pipelines to support rapid, reliable deployment of AI/ML models and related services.
- Deploy and manage AI/ML workloads across one or more major cloud platforms (AWS, Azure, GCP, OCI), leveraging native AI/ML services as appropriate.
- Implement MLOps best practices, including experiment tracking, model registry, feature stores, monitoring, and automated retraining where appropriate.
- Optimize model performance and cost through techniques such as quantization, pruning, distillation, and efficient distributed training.
- Integrate and operationalize LLM and NLP solutions (e.g., transformers, RAG systems) to support text understanding, summarization, Q\&A, and other intelligent automation use cases.
- Collaborate with data engineers, cloud engineers, and domain experts to design robust data pipelines and architectures for AI/ML workloads, including time‑series, image/video, and text data.
- Ensure that all solutions adhere to security, compliance, and governance standards, especially when working with sensitive or regulated data.
- Provide technical leadership, mentorship, and guidance to junior engineers and peers, promoting best practices in AI/ML engineering, DevOps, and software craftsmanship.
- Produce high‑quality technical documentation, including architecture diagrams, API specifications, deployment runbooks, and user guides.
- Participate in technical planning, backlog grooming, and estimation; contribute to roadmap development for AI/ML capabilities.
Other duties as assigned
Here’s What You Need
The qualifications and skills listed below are intended to provide a general overview of the requirements for this position. However, due to the anticipated nature of the contract and the absence of a finalized task order from the client, this list should not be considered all\-encompassing. Additional qualifications, certifications, skills, or experience specific to the client’s requirements may be identified and requested upon award of the task order. Candidates should demonstrate flexibility and a willingness to adapt to evolving responsibilities as outlined by the client.* 8\+ years of professional software engineering experience, with substantial work in AI/ML and cloud‑native development
- Experience with at least one major cloud platform (AWS, Azure, GCP, or OCI) for deploying and managing ML workloads
- Hands‑on experience with cloud AI/ML services such as Azure AI, AWS SageMaker/Bedrock, GCP Vertex AI, or OCI AI Services
- Strong DevOps background, including:
+ Ansible for configuration management and automation
+ Docker for containerization
+ Kubernetes for container orchestration
+ CI/CD best practices for automated build, test, and deployment
- Proficiency with relational and non‑relational databases, including:
+ SQL (PostgreSQL, MySQL)
+ NoSQL and vector databases for similarity search and embedding‑based retrieval
- Strong scripting skills in both:
+ Bash
+ PowerShell
- Proven experience designing and maintaining CI/CD pipelines using:
+ Azure DevOps
+ GitHub Actions
+ Jenkins
+ Or similar automation tools
- 3–5\+ years of production‑level Python development (primary implementation language)
- 3\+ years of experience with NLP and LLMs, including:
+ Transformer models (BERT, GPT, T5, etc.)
+ RAG (Retrieval‑Augmented Generation) systems
+ Fine‑tuning and prompt engineering
+ Building LLM‑based applications
- 3\+ years of experience with time‑series data, including:
+ Forecasting models
+ Anomaly detection
+ Sequential data modeling
+ Real‑time monitoring systems
- 3\+ years of experience building recommender systems, such as:
+ Collaborative filtering
+ Ranking models
+ Personalization engines
+ Content recommendation pipelines
- Production experience with MLOps tools and platforms, such as:
+ MLflow
+ Weights \& Biases
+ Kubeflow
+ Airflow
+ Or similar systems for orchestration, tracking, and model lifecycle management
- Experience with distributed training, including:
+ Large‑scale model training
+ Multi‑GPU and/or multi‑node setups
+ Data/model parallelism and performance optimization
- Production computer vision experience using:
+ PyTorch and/or TensorFlow
+ OpenCV
+ YOLO or similar frameworks for object detection and segmentation
+ Real‑time inference and deployment workflows
- Experience with feature stores (e.g., Feast, Tecton) and/or advanced feature engineering techniques
- Hands‑on experience with model optimization techniques:
+ Quantization
+ Pruning
+ Knowledge distillation
- Experience working with LLM ecosystems such as:
+ Ollama
+ Hugging Face
+ Other non‑frontier / open‑weight models
- Demonstrated AI/ML production track record:
+ Built and deployed at least 2–3\+ ML models serving real users (beyond experimental or research‑only projects)
- Must be able to pass a background check. May require additional background checks as required by projects and/or clients at any time during employment.
Minimum Skills:* Exceptional interpersonal skills with the ability to communicate in a clear, professional, and articulate manner.
- Exceptional verbal and written communication skills.
- Excellent organizational, analytical, and problem\-solving skills with high\-level attention to detail.
- Proven ability to multitask and prioritize in a fast past environment with changing priorities; adaptable to change and a quick learner.
- Must be self\-motivated and able to work well independently as well as on a multi\-functional team.
- Ability to handle sensitive and confidential information appropriately
- Proficient in MS Office, Word, Outlook, PowerPoint, and Excel.
Desired Qualifications:* 1\+ year of experience with Geospatial Information Systems (GIS) and analyzing or modeling spatial data
- Prior experience in one or more of the following domains:
+ Transportation
+ Logistics
+ Smart city or urban infrastructure
- Background applying computer vision to infrastructure or vehicular data, including:
+ Object detection
+ Image segmentation
+ Video or sensor data analysis
- Familiarity with public sector data compliance, security, and governance, such as:
+ Data classification and handling
+ Access control and audit requirements
+ Regulatory and policy constraints for government data
- Experience with Unreal Engine in the context of:
+ Real‑world digital twinning
+ Simulation or immersive visualization of physical environments
- Experience integrating or building solutions with:
+ Google Maps APIs
+ Cesium or similar 3D mapping/geospatial visualization platforms
- Experience with Polygonflow Dash and its capabilities for:
+ 3D workflows
+ Visualization pipelines
+ Automation of complex modeling or simulation tasks
Our Commitment to you / overview of benefits* Medical, Dental and Vision Insurance; Wellness Program
- Flexible Spending Accounts (Healthcare, Dependent Care, Commuter)
- Short\-Term and Long\-Term Disability options
- Basic Life and AD\&D Insurance (Company Provided)
- Voluntary Life and AD\&D options
- 401(k) Retirement Savings Plan with matching after one year
- Paid Time Off
Reports to: Program Manager
Working Conditions* Professional office environment with the ability to work on\-site at client facility.
- Must be physically and mentally able to perform duties extended periods of time.
- Ability to use a computer and other office productivity tools with sufficient speed to meet the demands of this position.
- Must be able to establish a productive and professional workspace.
- Must be able to sit for long periods of time looking at computer screen.
- May be asked to work a flexible schedule which may include holidays.
- May be asked to travel for business or professional development purposes.
- May be asked to work hours outside of normal business hours.
Other Duties: *Please note this job description is not designed to cover or contain a comprehensive list of activities, duties or responsibilities that are required of the employee for this job. Duties, responsibilities, and activities may change at any time with or without notice.*
*Cayuse is an Equal Opportunity Employer. All employment decisions are based on merit, qualifications, skills, and abilities. All qualified applicants will receive consideration for employment in accordance with any applicable federal, state, or local law.*
Salary Context
This $116K-$166K range is in the lower quartile for AI Software Engineer roles in our dataset (median: $190K across 219 roles with salary data).
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 3,823 AI roles we're tracking, AI Software Engineer positions make up 7% of the market. At Cayuse Holdings, 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 $232,000 based on 797 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($141K) sits 39% below the category median. Disclosed range: $116K to $166K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Cayuse Holdings AI Hiring
Cayuse Holdings has 2 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer. Positions span Washington, DC, US, Austin, TX, US. Compensation range: $166K - $170K.
Location Context
AI roles in Austin pay a median of $215,300 across 523 tracked positions. That's 8% above the national 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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