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About This Role
About the Role
We are looking for a talented AI Software Engineer to design, build, and deploy intelligent software products powered by modern AI and machine learning technologies while also setting up API connections for direct data communications with our customers.
You will work across the full software development lifecycle—from designing backend systems and APIs to integrating large language models (LLMs), building AI-powered features, and deploying reliable production systems. The ideal candidate combines strong software engineering fundamentals with practical experience building AI applications.
Responsibilities
- Design, develop, test, and maintain scalable software applications incorporating AI/ML capabilities.
- Build and integrate applications using LLMs, generative AI, RAG, embeddings, and AI agents.
- Develop robust APIs, backend services, data pipelines, and AI infrastructure.
- Integrate AI models from providers such as OpenAI, Anthropic, Google, or open-source models.
- Build production-quality prompt, evaluation, and model-integration pipelines.
- Develop systems for document processing, semantic search, vector databases, and retrieval-augmented generation.
- Optimize AI applications for accuracy, latency, scalability, and cost.
- Implement automated testing, monitoring, logging, security, and observability.
- Collaborate with product managers, designers, data scientists, and other engineers.
- Translate business requirements into reliable technical solutions.
- Stay current with emerging AI technologies and identify opportunities to apply them to products and internal workflows.
Required Qualifications
- Bachelor’s degree in computer science, Engineering, or equivalent practical experience.
- 3+ years of professional software engineering experience.
- Strong proficiency in Python and/or TypeScript/JavaScript.
- Experience building and deploying production software and APIs.
Strong understanding of software architecture, data structures, algorithms, and system design.
Hands-on experience with LLMs or generative AI applications.
Experience with REST APIs and/or GraphQL.
Experience with relational and/or NoSQL databases.
Familiarity with Git, CI/CD, automated testing, and cloud environments.
Strong debugging, problem-solving, and communication skills.
Preferred Qualifications
Experience with production operations and oil field chemistries.
Experience with RAG, vector databases, embeddings, prompt engineering, and AI agents.
Experience with frameworks such as LangChain, LlamaIndex, or similar technologies.
Experience with AWS, Azure, or Google Cloud.
Experience with Docker and Kubernetes.
Experience with ML/AI frameworks such as PyTorch or TensorFlow.
Experience building evaluation frameworks for LLM applications.
Experience with model fine-tuning, inference optimization, or open-source models.
Experience with data engineering or real-time data pipelines.
Experience taking AI prototypes from concept to production.
What We Offer
Competitive salary and benefits.
Opportunity to work on meaningful AI products from the ground up.
Highly collaborative engineering environment.
Access to modern AI and cloud technologies.
Professional development and learning opportunities.
[Bonus / other benefits, if applicable].
How to Apply
Please submit your resume along with examples of relevant projects, GitHub repositories, or other work demonstrating your experience building software and AI-powered applications.
This company reserves the right to modify, interpret, or apply this job description in any way the company desires. This job description in no way implies that these are the only duties, including essential duties, to be performed by the employee occupying this position. THIS JOB DESCRIPTION IS NOT AN EMPLOYMENT CONTRACT, IMPLIED OR OTHERWISE. THE COMPANY REMAINS AN “AT-WILL” EMPLOYER. Qualified employees who require reasonable accommodations to perform the essential functions of the position should notify the Human Resources Director.
Equal Opportunity Employer
This employer is required to notify all applicants of their rights pursuant to federal employment laws. For further information, please review the Know Your Rights (https://www.eeoc.gov/poster) notice from the Department of Labor.
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 9,653 AI roles we're tracking, AI Software Engineer positions make up 5% of the market. At Jacam Catalyst, 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. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. 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: $272,275; VP: $235,000.
Jacam Catalyst AI Hiring
Jacam Catalyst has 1 open AI role right now. They're hiring across AI Software Engineer. Based in Midland, TX, US.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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.
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