Jobs at EvolutionIQ

Investigate and resolve technical customer tickets, collaborate with Solutions Engineering on analytics and data insights, support client deliverables, track SLAs, and identify ticket trends that inform product and engineering priorities. The role bridges Technical Support, Product Management, Engineering, DevOps, and client-facing teams while using Python and SQL for investigations.

Provide frontline technical support for an enterprise AI SaaS platform through email, chat, and ticketing systems. Diagnose issues, replicate bugs, query databases with SQL, investigate data discrepancies, and escalate complex problems. Educate nontechnical clients, manage cases in Salesforce and Zendesk, document solutions, optimize workflows, and share customer feedback with Product and Engineering. The role is suited to an early-career professional with a bachelor’s degree and interest in AI, SaaS, and technical product support.

Lead data science and analytics initiatives supporting product, engineering, leadership, and insurance clients. Analyze claims and product data, design experiments, generate hypotheses, measure ROI, build dashboards and reproducible analytics, and communicate actionable recommendations. Mentor junior analysts, conduct client-facing analyses, collaborate with ML teams, investigate data issues, and shape product strategy through sophisticated statistical modeling and exploratory analysis.

Lead end-to-end development of AI and machine learning solutions for a medical claims synthesis product. Design and deploy LLM-driven features, hybrid statistical ML and RAG systems, document extraction, and human-in-the-loop summarization workflows. Make architecture decisions, optimize production systems, evaluate model performance, collaborate with SMEs and product teams, and mentor engineers. Translate emerging AI research into reliable, scalable production applications.

Lead analytical projects that translate complex claims data into actionable insights for business strategy and client outcomes. Develop SQL queries, Python tools, dbt models, automated reporting, ROI analyses, and Looker dashboards. Define KPIs, ensure data quality, mentor junior analysts, and partner with clients and cross-functional leaders to explain AI recommendations, uncover business problems, and influence product direction, client retention, and opportunity prioritization.

Hands-on staff engineer who architects and builds backend systems integrating AI into insurance claims products. Modernize legacy platforms, design multi-tenant relational backends, prototype and productionize features, set engineering standards, and collaborate closely with Product, Data Science, and Data Engineering.

Hands-on Senior Software Engineer building and modernizing enterprise SaaS backend systems. Deliver end-to-end features, partner with Product and Data teams, use AI-assisted engineering tools for rapid prototyping and production work, write design docs, perform code reviews, and improve engineering standards to enhance the insurance claims experience.

Lead and scale the EIQ engineering organization, set technical vision and architecture, partner with Product, hire and develop leaders and ICs, own CI/CD and cloud infrastructure (GCP/AWS), drive strategic roadmaps, and represent engineering in executive discussions while embedding LLM/agentic workflows.

Lead enterprise product deployments, training, and change management for EIQ's AI claims guidance. Act as product and claims operations subject-matter expert, drive client adoption, partner with Sales/Product/Analytics, build playbooks, and improve operating models to increase client satisfaction, retention, and expansion.

Lead roadmap and execution for AI-first P&C claims products. Build and ship prototypes, work directly with enterprise carrier stakeholders, validate concepts with customers, partner with sales/customer success/marketing for GTM, contribute technically by coding prototypes, and drive strategic direction and business outcomes across multiple product lines.

Design, build, and own a multi-tenant enterprise SaaS backend that feeds ML data and BI. Collaborate with product, data science, UX, and users to deliver scalable, reliable customer-facing AI solutions. Drive systems design, database schemas, development lifecycle standards, technical docs, and code reviews while using AI/agentic tools to accelerate development.

Design, build, and deploy production ML models (time series forecasting and NLP). Perform EDA, feature engineering, model evaluation, and translate business problems into ML solutions. Maintain production model-serving pipelines and collaborate across teams to deliver scalable data-driven systems.

Translate complex claims and model data into trustworthy, scalable reporting and visualizations. Build SQL and Python-based tools, contribute to GitHub, create Looker/Tableau/PowerBI dashboards, and synthesize insights for stakeholders to drive product, client, and operational decisions.

The Technical Engagement Manager oversees client project delivery, enhances operational product success, and manages timelines while ensuring client satisfaction through effective communication and data analysis.

Lead pre-sale solutions consultant for enterprise insurance SaaS agentic products. Drive discovery, solution architecture, integration planning, business case development, and executive alignment across Sales, Product, and Engineering to enable complex, integration-heavy deployments.