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.
**Applicants must be local OR be willing to relocate to the Bonita Springs area within a reasonable time frame following their start date.
Position Summary
AI-native Software Engineer with hands-on experience delivering real-world AI-powered products.
This role is ideal for a mid-level developer who:
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Has contributed to 3–5 production AI projects
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Understands how to move AI systems from prototype to secure, scalable healthcare applications
You will:
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Design and ship AI-driven features across our ophthalmology platform
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Work with third-party LLM integrations
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Develop custom ML models
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Build domain-specific enhancements using clinical data
This is not a research-only role. We are looking for someone who has built, integrated, evaluated, and deployed AI systems in production environments.
What You’ll Do:
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Develop and maintain backend services and APIs using .NET/C# (.NET Core, .NET 8+)
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Build responsive, user-friendly interfaces using HTML/CSS
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Design AI-enabled workflows that integrate safely into clinical software
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Collaborate with product, clinical, and engineering teams
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Establish and participate in code review processes
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Work within an agile framework, contributing to:
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Sprint planning
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Daily standups
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Retrospectives
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Write clean, maintainable, testable code
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Troubleshoot distributed systems and AI pipelines
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Contribute to architectural decisions around AI infrastructure and model evaluation
AI & Machine Learning Responsibilities:
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Design and implement production-grade AI services
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Integrate third-party LLMs:
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OpenAI
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Anthropic
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Azure OpenAI
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Hugging Face
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Build and fine-tune ML models:
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NLP
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Structured data models
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Computer vision (where appropriate)
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Enhance foundation models using:
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RAG
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Fine-tuning
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Embeddings
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Adapters
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Design evaluation frameworks to measure:
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Accuracy
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Reliability
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Hallucination rates
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Clinical relevance
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Implement retrieval pipelines using vector databases
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Develop prompt engineering strategies with testing and versioning
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Optimize model performance, latency, and cost
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Contribute to reinforcement learning or simulation experimentation (Gymnasium a plus)
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Collaborate on model deployment, monitoring, and drift detection
Required Qualifications:
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3–7 years of professional software development experience
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Hands-on contribution to 3–5 AI/ML production or near-production projects
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Experience integrating LLM APIs into real systems
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Experience building or fine-tuning ML models
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Experience working with structured and unstructured datasets
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Strong understanding of model evaluation and production tradeoffs
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Experience with cloud platforms (AWS, Azure, or GCP)
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Solid foundation in:
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Data structures
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Algorithms
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APIs
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Distributed system design
Technical Experience:
Backend
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Strong experience with .NET/C# (.NET Core and/or .NET 8+)
Frontend
AI/ML
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PyTorch
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TensorFlow
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Scikit-learn (or similar frameworks)
Data
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Embeddings
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Vector databases (Pinecone, FAISS, Weaviate)
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Semantic search
Cloud & DevOps
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Deploying AI services using containers, serverless, or managed ML services
Team & Process Experience:
Experience working in collaborative environments with exposure to:
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Git version control and branching strategies
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Agile methodologies (Scrum/Kanban)
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Task/story management tools
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Code reviews
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Architectural discussions
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Cross-functional collaboration
Mindset & Collaboration:
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AI-native mindset (data, models, feedback loops, iteration)
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Pragmatic builder who understands production constraints
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Comfortable with ambiguity in emerging AI spaces
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Strong communicator, especially explaining AI tradeoffs
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Motivated to apply AI in healthcare where safety and reliability matter
Nice to Have:
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Computer vision experience (especially medical imaging)
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Experience with clinical or regulated datasets (HIPAA familiarity)
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MLOps experience:
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Model versioning
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Experiment tracking
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Monitoring
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CI/CD for ML
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Experience with Gymnasium or reinforcement learning
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Designing AI evaluation benchmarks
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Understanding OAuth and systems integration patterns
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Experience with RESTful API design
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Knowledge of SQL Server and Entity Framework
What We Offer:
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Opportunity to build real-world AI products in healthcare
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Ownership over meaningful AI initiatives
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Join a growing team at an exciting inflection point
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Collaborative environment where your architectural input matters
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Exposure to diverse AI approaches:
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LLM integration
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Custom ML
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Retrieval systems
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Domain-adapted models
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Professional development opportunities
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Work on challenging, mission-driven problems
Our Ideal Candidate
You’ve shipped AI features that users rely on.
You understand:
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Model limitations
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Evaluation tradeoffs
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Production constraints
You are:
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Curious
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Technically rigorous
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Thoughtful about AI application in healthcare
