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EvolutionIQ is building state-of-the-art AI technology to transform insurance claims handling, making the process more accurate, fair, and efficient. The company has experienced rapid growth and is recognized as one of Inc.'s Best Workplaces for three consecutive years.
As a Senior AI/ML Engineer specializing in LLMs, you will take ownership of end-to-end AI/ML projects for the company's medical synthesis product. You'll lead from ideation through requirements gathering, architecture design, implementation, deployment, and ongoing optimization. The role emphasizes high autonomy, thoughtful technical decision-making, and guiding the direction of AI/ML solutions across the product.
Key responsibilities include:
- Leading AI/ML projects end-to-end with full ownership and accountability
- Making high-level architectural and technical decisions balancing scalability, performance, reliability, and business impact
- Designing and deploying LLM-driven features for claim synthesis, including information extraction from complex medical documents and human-in-the-loop summarization workflows
- Developing hybrid machine learning solutions combining statistical models, LLMs, RAG, and embeddings-based retrieval techniques
- Writing clean, scalable, efficient code while optimizing production AI/ML systems
- Collaborating with data labelers and subject matter experts to evaluate outputs and improve model performance
- Partnering with Product, Engineering, and cross-functional teams on requirements, technical approaches, and rapid iteration
- Providing technical leadership and guidance to other engineers, establishing best practices
- Breaking down complex problems into well-defined technical solutions
- Translating cutting-edge AI/ML research into production-grade, reliable solutions for live customer environments
Required qualifications: 5-8+ years writing performant Python code; minimum 1 year building and deploying LLM-powered products in professional environments; proven experience leading technical projects from concept to production; expertise in architectural decisions for AI/ML systems; hands-on experience with statistical ML and hybrid approaches (LLMs, RAG, embeddings); ability to build and integrate API services in microservice architectures; strong skills evaluating LLM outputs and model predictions; expertise in prompt engineering and fine-tuning for domain-specific applications; strong communication and collaboration skills.
Bonus qualifications include translating state-of-the-art research to production code, collaborating with data labelers on training data, building agentic AI systems, experience with multimodal data, and mentoring engineers.
