Jobs at EvolutionIQ

Design, build, and launch scalable enterprise applications, backend architectures, and data pipelines supporting multi-tenant users. Use agentic coding tools to accelerate development while enforcing architecture, security, quality, and deployment standards. Partner with product, client services, and data science teams to convert pipeline outputs into business value. Responsibilities include technical design, code reviews, performance optimization, data quality checks, rapid prototyping, and production engineering.

Build and scale a multi-tenant enterprise SaaS claims application using Python, backend REST services, relational databases, and LLM-powered capabilities. Own the full feature lifecycle, architecture, deployments, security, code quality, and technical roadmap. Collaborate with product, design, data science, client services, and end users while evaluating AI outputs and translating insurance business needs into reliable production solutions.

Build and own a full-stack, multi-tenant enterprise SaaS application for AI-powered insurance solutions. Responsibilities include expert Python backend development, REST services, frontend work, prompt engineering, database design, scalable architecture, feature delivery, and collaboration with product, data science, UX, and client-facing teams. The role also involves adopting AI development tools, setting engineering standards, and shaping the product roadmap.

Owns architecture, delivery, evaluation, security, and operation of LLM-powered systems and agentic workflows for insurance customers. Designs NLP pipelines, durable event-driven systems, deterministic orchestration, tracing, auditability, privacy controls, and tenant isolation. Establishes evaluation and guardrail strategies, reviews designs and code, authors technical documentation, mentors engineers, collaborates with product and data science teams, and participates in on-call operations.

Build and operate LLM-powered features and agentic workflows for insurance claims using Python and TypeScript. Develop evaluations, guardrails, retrieval and NLP pipelines, durable event-driven services, observability, and secure multi-tenant architectures. Work across relational, document, and graph databases, cloud platforms, and workflow engines. Own features from design through production, participate in on-call, and collaborate with product and data science teams.

Build and operate backend services and durable event-driven systems connecting AI agents to insurance claims workflows. Own distributed-systems reliability, failure handling, tracing, auditability, data modeling, tenant isolation, cloud infrastructure, and production operations. Author cross-team designs, review code, mentor engineers, collaborate with product and data science teams, and participate in on-call rotation. Improve engineering practices for agentic coding tools while delivering measurable, scalable services.

The Director, Data Insights serves as the primary data advisor to executives and external stakeholders. Responsibilities include translating analytics into board-ready narratives, influencing strategic decisions, aligning data roadmaps with business value, resolving cross-functional priorities, promoting data-driven culture, and conducting rapid analyses using agentic tools.

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.

Own full-cycle recruiting for complex Engineering and AI roles, including sourcing, screening, stakeholder partnership, candidate engagement, and offer management. Advise Engineering leaders on talent markets, compensation, hiring strategies, and realistic candidate profiles. Build passive technical talent pipelines using LinkedIn Recruiter, GitHub, Boolean search, networking, and AI-enabled tools. Track recruiting analytics, improve interview processes, and mentor other recruiters.

Source and recruit talent across Sales, Client Success, Shared Services, Product, Engineering, and other functions. Build candidate pipelines using job ads, sourcing tools, and LinkedIn Recruiter; create compelling outreach; track sourcing metrics and conduct A/B testing; communicate progress to hiring managers; and coordinate candidate interviews. The role requires 5+ years of recruiting experience, strong organization, data-driven sourcing, and the ability to attract passive candidates to a growing AI company.

Advise enterprise SaaS clients on maximizing EvolutionIQ’s AI products through deployment support, impact measurement, consultative analytics, dashboards, statistical analysis, and executive presentations. Prototype reports, alerts, metrics, and product features with Product and Engineering teams. Identify industry trends, communicate strategic insights, improve client outcomes, and support Sales as a product and solutions expert.

Lead product design for an agentic AI platform serving insurance claims teams. Define human-AI interactions, automation and control models, information architecture, workflows, reusable agentic patterns, and design-system foundations. Conduct client discovery, prototyping, and usability testing while shaping experience strategy across complex, regulated problems. Partner with Product, Engineering, leadership, and clients to balance user needs, AI capabilities, technical feasibility, and regulatory requirements.

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.

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.