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About This Role
About Optivate
Optivate is a leading provider of healthcare technology software solutions purpose\-built for ophthalmologists and eye care specialists. The company's solutions, which include EMR, practice management, patient engagement, image management, and RCM and billing services are designed to streamline clinical documentation workflows and improve daily practice efficiencies for eye care professionals.
Position *S*ummary
We're hiring a full\-stack .NET engineer who is AI\-native — someone who builds production software end to end and who works fluently with modern AI development tooling and agentic workflows.
"AI\-native" here means two things, and we care about both:
- You build with AI. You use AI\-assisted and agentic development tooling (e.g., Cursor, Claude Code, Copilot) as a core part of how you work — including multi\-agent workflows where separate agents handle decomposition, architecture, development, QA, and code review. You know how to drive these tools to ship faster without sacrificing quality.
- You build AI into products. You're comfortable integrating LLMs and ML capabilities into real applications, and you understand the production and safety constraints that come with doing that in healthcare.
The bulk of this role is full\-stack .NET product engineering. A meaningful and growing portion involves designing and shipping AI\-powered features across our platform. This is hands\-on building role, not a research role — we're looking for someone who has built, integrated, and shipped software that users depend on, and who reaches for AI tooling instinctively to do it well.
You will:
- Design and ship AI\-driven features across our ophthalmology platform
- Work with third\-party LLM integrations
- Develop custom ML models
- Build domain\-specific enhancements using clinical data
This is not a research role. We are looking for someone who has built, integrated, evaluated, and deployed AI systems in production environments.
What You’ll Do:
- Design, build, and maintain full\-stack features across our ophthalmology platform — backend services and APIs in .NET/C\# (.NET Core, .NET 10\) and responsive frontend interfaces in HTML/CSS
- Use AI\-native and agentic development workflows (e.g., Cursor multi\-agent pipelines for decomposition, architecture, implementation, QA, and review) to move quickly while keeping code clean and reliable
- Design and integrate AI\-powered features — including third\-party LLM integrations — that fit safely into clinical software
- Troubleshoot and improve distributed systems
- Establish and participate in code review processes
- Work within an agile framework, contributing to:
- + Sprint planning
+ Daily standups
+ Retrospectives
- Write clean, maintainable, testable code, and establish and participate in code review
- Collaborate across product, clinical, and engineering teams
- Contribute to architectural decisions, including where and how AI fits into our systems
- Work within an agile framework: sprint planning, daily standups, and retrospectives AI Responsibilities
Depending on the work in front of us, you may:
- Integrate third\-party LLMs (OpenAI, Anthropic, Azure OpenAI, Hugging Face) into production features
- Build retrieval pipelines using embeddings and vector databases (Pinecone, FAISS, Weaviate) and implement semantic search
- Develop prompt engineering strategies with testing and versioning
- Design evaluation approaches that measure accuracy, reliability, hallucination rates, and clinical relevance
- Collaborate on deploying, monitoring, and maintaining AI services in production
- Optimize AI feature performance, latency, and cost
Required Qualifications:
- 3–7 years of professional software development experience with strong full\-stack delivery
- Strong, current experience with .NET/C\# (.NET Core and/or .NET 10\)
- Proficiency building responsive UIs with HTML/CSS
- Demonstrated fluency with AI\-assisted and agentic development tooling (e.g., Cursor, Claude Code, Copilot), ideally including multi\-agent development workflows
- Experience integrating LLM APIs into real, shipped systems (does not need to be the bulk of your career — but you've done it and understand the tradeoffs)
- Solid foundation in data structures, algorithms, API design, and distributed system design
- Experience with at least one major cloud platform (AWS, Azure, or GCP)
- Familiarity with model limitations, evaluation tradeoffs, and the realities of running AI in production
Team \& Process Experience:
Experience working in collaborative environments with exposure to:
- Git version control and branching strategies
- Agile methodologies (Scrum/Kanban)
- Task/story management tools
- Code reviews
- Architectural discussions
- Cross\-functional collaboration
Mindset \& Collaboration:
- AI\-native mindset (data, models, feedback loops, iteration)
- Pragmatic builder who understands production constraints
- Comfortable with ambiguity in emerging AI spaces
- Strong communicator, especially explaining AI tradeoffs
- Motivated to apply AI in healthcare where safety and reliability matter
Nice to Have:
- Experience with Blazor, particularly Blazor Hybrid and .NET MAUI for building cross\-platform applications on a shared .NET codebase
- Experience building or fine\-tuning ML models (NLP, structured data, computer vision)
- Enhancing foundation models with RAG, fine\-tuning, embeddings, or adapters
- Computer vision experience, especially medical imaging
- Experience with clinical or regulated datasets; HIPAA familiarity
- MLOps experience: model versioning, experiment tracking, monitoring, CI/CD for ML
- Designing AI evaluation benchmarks
- Reinforcement learning or simulation experimentation (Gymnasium a plus)
- OAuth and systems integration patterns; RESTful API design
- Git branching strategies and experience in Scrum/Kanban environments
What We Offer:
- A core role building real software that eye care practices depend on every day
- The chance to work AI\-native — both shipping AI\-powered features and using cutting\-edge agentic development tooling as part of how we build
- Ownership over meaningful initiatives and a voice in architectural decisions
- A collaborative team at an exciting inflection point
- Professional development opportunities
- Challenging, mission\-driven problems in healthcare
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 Rippling, 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.
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.
Rippling AI Hiring
Rippling has 19 open AI roles right now. They're hiring across AI Product Manager, AI Software Engineer, AI/ML Engineer, Data Engineer. Positions span Remote, US, New York, NY, US, San Francisco, CA, US. Compensation range: $60K - $330K.
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.
Frequently Asked Questions
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