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About This Role
ArcheSys is a technology consulting firm delivering innovative cloud, AI, DevSecOps, and digital modernization solutions to Federal, State, and Commercial customers. We help organizations accelerate mission outcomes through cloud\-native engineering, intelligent automation, and secure software development.
We're seeking a AI Software Engineer who is passionate about building production\-grade AI applications using Large Language Models (LLMs), Retrieval\-Augmented Generation (RAG), and Agentic AI. This is a hands\-on engineering role where you'll design, develop, deploy, and optimize secure AI solutions running on AWS.
This is a fully remote, full\-time position offering the opportunity to work on emerging AI technologies that support mission\-critical public sector initiatives.
Location: This is a remote position.
Residency Requirement: Candidates MUST have lived in the U.S. for at least 3 of the past 5 years and be authorized to work in the U.S. (Citizen, Permanent Resident, or EAD).
Sponsorship: This position does not offer any type of sponsorship. Candidates must already be authorized to work in the U.S. (e.g., Citizen, Permanent Resident, or EAD).
Clearance: Public Trust Clearance (or ability to obtain)
### Key Responsibilities
AI Application Development
- Design, develop, and maintain AI\-powered applications using Python.
- Build intelligent applications leveraging OpenAI APIs, Amazon Bedrock, and other enterprise LLM platforms.
- Design and implement Retrieval\-Augmented Generation (RAG) architectures using enterprise knowledge sources and vector databases.
- Develop Agentic AI workflows capable of autonomous reasoning, planning, and task execution.
- Build reusable AI services, APIs, and software components for enterprise applications.
- Optimize prompts, model interactions, and application performance for reliability, scalability, and cost efficiency.
- Leverage AI development tools safely and effectively, taking ultimate human accountability for the design, security, and test coverage of all generated code.
Cloud \& Infrastructure Engineering
- Design, deploy, and maintain AI solutions on AWS.
- Build Infrastructure as Code (IaC) using Terraform.
- Develop containerized applications using Docker.
- Deploy and manage workloads on Kubernetes.
- Collaborate with DevOps engineers to automate deployments through CI/CD pipelines.
- Ensure AI solutions meet security, scalability, and operational requirements.
Software Engineering \& Integration
- Develop secure REST APIs and backend services.
- Integrate AI capabilities with enterprise applications, cloud services, and external APIs.
- Write clean, maintainable, well\-tested, and reusable code following software engineering best practices.
- Participate in architecture reviews, code reviews, and technical design discussions.
Innovation \& Research
- Evaluate emerging AI frameworks, tools, and technologies.
- Prototype new AI capabilities and rapidly validate proof\-of\-concepts.
- Recommend improvements that increase developer productivity and enhance customer outcomes.
- Help establish reusable AI patterns, templates, and best practices across projects.
Documentation \& Collaboration
- Create technical documentation, architecture diagrams, implementation guides, and operational runbooks.
- Work closely with Solution Architects, Cloud Engineers, Product Managers, and customers to translate business requirements into AI solutions.
- Participate in Agile ceremonies, sprint planning, backlog refinement, and knowledge\-sharing sessions.
### Required Qualifications
Education \& Experience
- Bachelor's degree in Computer Science, Software Engineering, Information Technology, or related discipline (or equivalent professional experience).
- 4–8 years of professional software engineering experience.
- 2\+ years of professional Python development experience.
- Must be a U.S Citizen or Green Card Holder.
Technical Skills
- Strong Python programming skills.
- Efficient prompt engineering
- Experience building applications on AWS.
- Experience integrating OpenAI APIs and/or Amazon Bedrock.
- Hands\-on experience implementing Retrieval\-Augmented Generation (RAG) solutions.
- Experience designing or developing Agentic AI workflows.
- Experience developing RESTful APIs.
- Experience using Terraform for Infrastructure as Code.
- Experience with Docker and Kubernetes.
- Experience using Git and modern CI/CD pipelines.
- Strong understanding of software design principles, API development, and cloud\-native architectures.
Preferred Qualifications
- Nice to have worked with LangChain, LangGraph, CrewAI, AutoGen, Semantic Kernel, or similar AI orchestration frameworks.
- Nice to have worked with vector databases such as Pinecone, Amazon OpenSearch, pgvector, or Weaviate.
- Experience with Amazon ECS, Amazon EKS, Lambda, API Gateway, DynamoDB, or S3\.
- Familiarity with prompt engineering, AI evaluation, and model observability.
- Experience with DevSecOps and secure software development practices.
- Nice to have experience supporting U.S. Federal Government customers.
- Familiarity with NIST 800\-53, FedRAMP, FISMA, or CMS security requirements is a plus.
What We're Looking For
- Passion for solving complex business problems using AI.
- Curiosity to learn and experiment with emerging AI technologies.
- Strong analytical, troubleshooting, and problem\-solving skills.
- Excellent written and verbal communication skills.
- Ability to thrive in a collaborative, fast\-paced Agile environment.
- Customer\-first mindset with a commitment to delivering high\-quality software solutions.
Eligibility to Work at ArcheSys
- All work must be performed within the continental United States.
- Candidates must be legally authorized to work in the U.S. without sponsorship.
- Selected candidates may be required to complete background investigations and obtain government security authorization for certain projects.
- ArcheSys participates in E\-Verify.
What We Offer
- Competitive salary and comprehensive benefits package.
- Fully remote work environment.
- Opportunity to build next\-generation AI and cloud\-native solutions.
- Exposure to OpenAI, Amazon Bedrock, Agentic AI, and emerging AI technologies.
- Continuous learning through certifications, training, conferences, and professional development.
- Collaborative culture that encourages innovation, experimentation, and career growth.
- Opportunity to support impactful Federal and Commercial digital transformation initiatives.
Join us at Archesys and be part of a team dedicated to delivering cutting\-edge cloud solutions for clients in the public sector. Your expertise and passion for technology will help us continue to innovate and grow. We look forward to welcoming you to our team and supporting your success as AI Software Engineer.
Archesys participate in E\-Verify. Upon hire, we will provide the federal government with your Form I\-9 information to confirm that you are authorized to work in the U.S.
Archesys is an equal opportunity employer committed to creating a diverse and inclusive workplace. We welcome applications from all qualified candidates, regardless of race, color, religion, sex.
Salary Context
This $100K-$135K range is in the lower quartile for AI Software Engineer roles in our dataset (median: $183K across 194 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 3,708 AI roles we're tracking, AI Software Engineer positions make up 7% of the market. At Archesys, 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 $219,250 based on 424 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($117K) sits 46% below the category median. Disclosed range: $100K to $135K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Archesys AI Hiring
Archesys has 1 open AI role right now. They're hiring across AI Software Engineer. Based in Remote, US. Compensation range: $135K - $135K.
Remote Work Context
Remote AI roles pay a median of $185,334 across 717 positions. About 14% 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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