AI Solution Engineer
sapiens
Job Description
- 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.