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About This Role
Seeking a hands\-on AI Native Software Engineer to design, build, and deploy production\-grade AI\-driven systems within enterprise environments.
This role focuses on building production systems, agent\-based workflows, integrating AI platforms, and delivering scalable, cloud\-native solutions. You’ll work across the full lifecycle — from system design through to production deployment — building AI\-powered applications that integrate into real business workflows. This is a 100% hands\-on engineering role, requiring strong software engineering fundamentals alongside practical experience with modern AI systems.
##### What You Bring
- 8–10\+ years of software engineering experience
- Strong experience building cloud\-native systems, including APIs, microservices, containers, and serverless architectures
- Proven experience building and deploying AI/LLM\-based systems in production (e.g. RAG, agents, orchestration workflows)
- Hands\-on experience with AI platforms (e.g. OpenAI, Anthropic, Google Vertex, or similar)
- Experience designing and implementing:
- + Retrieval systems (RAG)
+ Agent workflows and orchestration
+ Tool/function invocation patterns
- Strong understanding of system\-level trade\-offs (performance, cost, latency, reliability)
- Experience with CI/CD pipelines, infrastructure as code, and production observability
- Proficiency in Python, Java, or similar backend languages
- Experience debugging and optimising production systems
##### Preferred Experience
- Experience with agent frameworks (e.g. LangGraph, AutoGen, CrewAI)
- Experience designing multi\-agent or distributed AI systems
- Familiarity with enterprise\-scale system integration
Experience optimising AI workloads for cost and performance
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##### What You'll Do
- Design and implement AI agents, including RAG pipelines, orchestration workflows, and tool invocation
- Build evaluation frameworks to measure system accuracy, latency, and reliability
- Implement observability and monitoring across the AI system lifecycle
- Integrate with AI providers and build abstraction layers to support multi\-model and multi\-provider architectures
- Optimise AI systems for performance, cost, and scalability
- Develop cloud\-native services using microservices, containers, and serverless patterns
- Build and deploy AI\-powered applications aligned to business workflows
- Integrate AI systems into existing enterprise platforms and APIs
- Define and execute testing strategies for AI systems
- Measure and improve system performance (latency, throughput, accuracy, cost)
- Debug and optimise production systems
- Collaborate with client and internal engineering teams
- Participate in technical design discussions, focused on implementation
##### About Rearc
At Rearc, we're committed to empowering engineers to build awesome products and experiences. Success as a business hinges on our people's ability to think freely, challenge the status quo, and speak up about alternative problem\-solving approaches. If you're an engineer driven by the desire to solve problems and make a difference, you're in the right place!
Our approach is simple — empower engineers with the best tools possible to make an impact within their industry.
We're on the lookout for engineers who thrive on ownership and freedom, possessing not just technical prowess, but also exceptional leadership skills. Our ideal candidates are hands\-on\-keyboard leaders who don't just talk the talk but also walk the walk, designing and building solutions that push the boundaries of cloud computing.
Founded in 2016, we pride ourselves on fostering an environment where creativity flourishes, bureaucracy is non\-existent, and individuals are encouraged to challenge the status quo. We're not just a company; we're a community of problem\-solvers dedicated to improving the lives of fellow software engineers.
Our commitment is simple \- finding the right fit for our team and cultivating a desire to make things better. If you're a cloud professional intrigued by our problem space and eager to make a difference, you've come to the right place. Join us, and let's solve problems together!
Your first few weeks at Rearc will be spent in an immersive learning environment where our team will help you get up to speed. Within the first few months, you’ll have the opportunity to experiment with a lot of different tools as you find your place on the team.
*Rearc is committed to a diverse and inclusive workplace. Rearc is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status.*
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 3,824 AI roles we're tracking, AI Software Engineer positions make up 7% of the market. At Rearc, 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 $234,620 based on 682 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $160,000.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
Rearc AI Hiring
Rearc has 1 open AI role right now. They're hiring across AI Software Engineer. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $169,035 across 1,817 positions. About 16% of all AI roles offer remote work.
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 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 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 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). 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 (119) are outnumbered by mid-level (1,813) and senior (1,472) 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 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 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 $200,000. Top-quartile roles start at $253,000, 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 $293,500 median, while Prompt Engineer roles sit at $142,800. 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 (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 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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