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About This Role
The Phia Group is a service\- oriented organization assisting employee health plans nationwide. We provide our clients with innovative cost\-cutting solutions and constantly expanding service offerings. We continue to enjoy growth thanks to our most valuable resource – our talented and committed team.
At The Phia Group, whose mission is to provide high quality yet affordable healthcare to American employees and their families, you can look forward to not only unparalleled benefits for yourself but also being immersed in a company that was named one of USA Today’s Top Workplaces for 2025. Meanwhile, from a regional perspective, both The Boston Globe and Louisville Business First also recognized our unwavering commitment to upholding an internal culture of inclusivity, enjoyment, and empathy for our valued employees by listing The Phia Group in their respective lists for the Top Places to Work in 2025\.
About the Role
We are seeking a forward\-thinking AI Software Engineer to design, build, and scale intelligent applications powered by modern AI technologies. This role goes beyond traditional software engineering — focusing on Generative AI, Retrieval\-Augmented Generation (RAG), and AI\-assisted development using tools like Claude, GPT, and other LLM platforms.
You will play a key role in shaping our AI roadmap by building production\-grade AI systems, optimizing developer productivity with AI tools, and integrating intelligent workflows into our healthcare platforms.
Key Responsibilities
AI Engineering \& GenAI Development
- Design and implement RAG (Retrieval\-Augmented Generation) pipelines using modern vector databases and embeddings.
- Build and optimize LLM\-powered applications (chatbots, copilots, automation agents).
- Leverage tools like Claude, GPT, and AI coding assistants to accelerate development and improve code quality.
- Develop agentic AI workflows using frameworks that enable reasoning, tool usage, and multi\-step decision\-making.
- Design and orchestrate multi\-agent workflows including orchestrator/supervisor patterns, agent handoffs, and tool routing
Azure AI \& Cloud Integration
- Architect and deploy AI solutions using Azure AI Foundry / Azure OpenAI / Azure ML.
- Manage scalable deployments using Azure Kubernetes Service (AKS), Docker, and Helm.
- Implement semantic search and vector indexing using tools like Qdrant or Azure Cognitive Search.
- Build model\-agnostic AI routing layers ( this is to abstract the LLM provider dependencies and create pipelines where we can change models on the fly)
Full\-Stack \+ AI Integration
- Build and maintain full\-stack applications integrating AI services:
+ Frontend: Angular / modern JS frameworks
+ Backend: FastAPI, Spring Boot, Node.js
+ Data: SQL/NoSQL systems
- Integrate AI services into existing enterprise systems and APIs.
AI\-Driven Development \& Productivity
- Use AI tools (Claude, Copilot, etc.) for:
+ Code generation and refactoring
+ Debugging and optimization
+ Test generation and documentation
- Continuously evaluate and adopt new AI tools to improve engineering velocity.
DevOps \& Scalability
- Build and maintain CI/CD pipelines (Azure DevOps) for AI applications.
- Optimize system performance, scalability, and reliability of AI workloads.
- Monitor and improve model performance, latency, and cost efficiency.
Required Qualifications
- Bachelor’s degree in Computer Science, IT, or related field \+ relevant experience.
- Strong experience with:
+ Python (FastAPI preferred)
+ Modern backend or full\-stack frameworks
- Hands\-on experience with:
+ RAG architecture, embeddings, and vector databases
+ LLMs (GPT, Claude, or similar)
- Experience deploying applications on Azure (AKS, Azure AI services).
- Familiarity with Docker, Kubernetes, and cloud\-native architectures.
- Experience with MS SQL, Azure storage, Azure App services, APIM.
Preferred Qualifications
- Experience with Azure AI Foundry or similar GenAI platforms.
- Strong understanding of:
+ Prompt engineering
+ Context management in LLMs
+ Fine\-tuning / evaluation techniques
- Experience with vector DBs like Qdrant, Pinecone, or Azure Cognitive Search.
- Exposure to agent frameworks (LangChain, Semantic Kernel, AutoGen, etc.).
- Prior experience building AI copilots or internal AI tools.
Working Conditions / Physical Demands
Sitting at workstation for prolong periods of time. Extensive computer work. Workstation may be exposed to overhead fluorescent lighting and air conditioning. Fast paced work environment. Operates office equipment including personal computer, copiers, and fax machines.
This job description is not intended to be and should not be construed as an all\-inclusive list of all the responsibilities, skills or working conditions associated with the position. While it is intended to accurately reflect the position activities and requirements, the company reserves the right to modify, add or remove duties and assign other duties as necessary.
External and internal applicants, as well as position incumbents who become disabled as defined under the Americans with Disabilities Act, must be able to perform the essential job functions (as listed here) either unaided or with the assistance of a reasonable accommodation to be determined by management on a case by case basis.
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 26,159 AI roles we're tracking, AI Software Engineer positions make up 2% of the market. At The Phia Group, 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 $235,100 based on 665 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $131,300.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
The Phia Group AI Hiring
The Phia Group has 2 open AI roles right now. They're hiring across AI Software Engineer. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $156,000 across 1,221 positions. About 7% 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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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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