AI Solution Engineer

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 centre 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.