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Job Description
Structured overview of role & requirementsAbout This Role
Develop and maintain AI/LLM components including prompt engineering, LLM API integrations (Anthropic, OpenAI, Azure OpenAI), and retrieval-augmented generation or classification pipelines mapping legacy actions to modern equivalents.
Integrate AI solutions with the ecosystem using REST APIs, webhooks, Azure CLI/Azure AD authentication, and MCP server endpoints.
Own quality through automated workflow validation tests and evaluation harnesses scoring model output fidelity; build and maintain XML/JSON-based parser/conversion engines for Nintex workflow to Power Automate logic.
Minimum Requirements
Proficient in Node.js/TypeScript or Python, with experience in XML/JSON parsing and building parsers, ASTs, or rule engines.
Experience with AI/LLM engineering: prompt engineering with schema-constrained output, LLM API integration (Anthropic, OpenAI, Azure OpenAI), and RAG or classification pipelines.
Knowledge of integration technologies: REST APIs, webhooks, Azure CLI, Azure AD authentication flows, and MCP server development.
Education: B.E./B.Tech/M.Sc./MCA or equivalent degree. Work Experience Required: Not explicitly mentioned in the JD.
Ideal Candidate Profile
Technical executor comfortable working end-to-end on AI/LLM component development with strong software engineering skills in Node.js/TypeScript or Python.
Experienced in complex parsing and transformation tasks involving XML/JSON and workflow systems like Nintex or Power Automate.
Familiar with Azure ecosystem and API integrations, with practical experience in building evaluation tests and quality assurance for AI model outputs.