Develop AI/LLM components — prompt engineering with schema-constrained output, LLM API integration (Anthropic/OpenAI/Azure OpenAI), and RAG or classification pipelines that map legacy actions to modern equivalents.
Integrate the solution with the surrounding ecosystem via REST APIs, webhooks, Azure CLI/Azure AD authentication flows, and MCP server endpoints.
Own quality through automated workflow validation tests and eval harnesses that score model output fidelity, and document unsupported or manually reviewed conversion cases.
What is it like working here?
- We recognize our profitability and project success comes from our team—great people doing great things. As such, we pursue profitable growth and expanded opportunities for our team.
- We offer challenging and diverse work across multiple industries and reward creativity and entrepreneurial innovation.
- We respect, encourage, and support each individual needs to continually learn and grow personally and professionally. We are committed to fostering our people.
- We listen. Every employee has something important to say that can contribute to enriching our environment.
- We compensate fairly. And while employees might come for the paycheck, they stay for the people. Our people are the reason we are exceptional. This is something we never forget.
Who can apply?
B.E./B.Tech/M.Sc./MCA
Prompt engineering / structured (schema-constrained) output generation
LLM API integration (Anthropic, OpenAI, Azure OpenAI)
RAG or classification pipeline experience
Coding Agent experience via Claude / Github copilot
Eval-harness / model output scoring experience
Skills you’ll bring
Node.js / TypeScript or Python (framework matching the Agent's stack)
XML/JSON parsing experience (Nintex NWF is XML-based)
Experience building parsers, ASTs, or rule engines
Regex / expression-language conversion experience