AI Solution Engineer

AI Solution Engineer:-

About the Role:-

Sapiens is scaling AI across its Sapiens insurance platform and the teams that build, test, and implement it. We re looking for an AI Solution Engineer to take our AI use cases from pilot to production - across automation, development, QA, and Implementation Engineering (IE). You ll work hands-on with LLM-based agents, MCP tooling, and Claude/Anthropic APIs to design, build, and scale systems that materially reduce manual effort across our client implementation projects. This is not a research role - it s a build-and-ship role. You ll own AI initiatives end-to-end: from proof of concept to a tool that Dev engineers and QA teams use every day.

• Develop & scale AI-powered tools for defect analysis, test automation, and performance engineering - turning working prototypes into reliable, project-agnostic systems used across multiple client implementations.
• Build and extend agentic workflows using Claude/Anthropic APIs, MCP servers, and orchestration patterns (e.g., Playwright-based agents, Jira/Confluence integrations, RAG pipelines etc).
• Support the Dev team directly - build AI tooling into the development workflow itself: code-review assistance, code and unit-test generation, legacy-code comprehension, and adoption of coding agents across development squads.
• Own the automation layer: design generic, reusable engines (not one-off scripts) - for example, extending browser automation frameworks into scalable, flows-based architectures.
• Partner with QA and Implementation Engineering to identify high-volume, repeatable problems (bug triage, root-cause classification, test coverage gaps, defect-to-process mapping) and turn them into AI-driven pipelines.
• Accelerate Implementation Engineering delivery - AI-assisted configuration and data-mapping validation, requirement-to-test traceability, and generation of implementation and handover documentation from project artifacts.
• Productionize AI initiatives: take internal tools from Claude-based prototypes to hardened services (e.g., Azure AI Foundry, API-based orchestration), with proper data validation, gating, and reporting.
• Build reporting & insight layers: structured system prompts, dashboards, and analytics that give leadership clear, quantified visibility into AI impact (hours saved, defect trends, coverage gaps).
• Drive AI governance: help define and maintain tracking standards for how AI is used and measured across Bug, Bug Customer, and Test workflows etc.
• Collaborate cross-functionally with QA, Dev, Performance, and Implementation teams across multiple concurrent client projects.

• 3+ years of hands-on software engineering experience, with real production code (not just notebooks/POCs).
• At least 2 years, Practical experience building with LLM APIs (Anthropic/Claude, OpenAI, or similar) - prompt engineering, tool use/function calling, agentic pipelines, RAG.
• Experience with MCP (Model Context Protocol) or similar tool-orchestration frameworks is a strong plus.
• Solid programming skills in Python and/or JavaScript/TypeScript (Node.js).
• Experience with test automation frameworks (Playwright, Selenium, or similar) and CI/test infrastructure.
• Experience building developer-facing tooling and working inside the SDLC (code review, version control workflows, CI/CD pipelines) - you understand how engineers work before you try to automate it.
• Familiarity with Jira/Confluence and working with structured project data at scale - advantage
• Comfort turning ambiguous business problems into scoped, shippable tools - you prototype fast, then productionize.
• Strong communication skills; comfortable working directly with QA, engineering, and leadership stakeholders across multiple projects.

• Background in the insurance industry (P&C, Core Systems, Policy/Claims/Billing) - advantage
• Experience with Azure AI Foundry or other enterprise AI orchestration platforms.

• Work at the center of Sapiens AI transformation - not on the sidelines of it.
• High ownership: your tools will be used across multiple live client implementations from day one.
• Direct collaboration with Dev engineers, QA, automation, and AI leadership.

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