Key skills –
GenAI / LLM Application Development
Prompt Engineering
Multi-Agent Architecture
Multi-Model Experience (GPT, Claude, Gemini, etc.)
AI Solution Architecture
RAG (Retrieval-Augmented Generation)
AI Guardrails & Governance
Function Calling / Structured Outputs
AI Evaluation & Testing

What you'll be doing
Design the multi-agent architecture— how an agent (system prompt, allowed model(s), validation rules, output shape) is defined, versioned, and selected at runtime, so adding the next agent to Client AI Studio doesn't mean re-architecting the platform.
Integrate multiple models behind a common interface, and make deliberate model-selection calls per task rather than defaulting to the biggest model available — e.g. Claude Sonnet for content drafting that needs judgement, a smaller/faster model for mechanical, high-volume, well-specified transforms . You'll need to be able to justify these choices, not just make them.
Own prompt engineering across every agent— the Jira Creation Agent's story/AC/DoD generation,



the KCI Notification Agent's content drafting per Medium (Email/SMS/Letter), and the "regenerate this one field" and "tell the AI what's wrong" refinement patterns used consistently across agents. Iterate based on real output quality, not first-draft-and-ship.
Define the AI service layer's contracts— the APIs the front-end and back-end developers integrate against ("generate," "regenerate field," "validate," "check duplicate," etc.) — working with the back-end developer, who owns the broader application backend, auth, and persistence.
Build guardrails, not just prompts: required-field validation, duplicate detection, and — critically — knowing where a deterministic system should do the work instead of an LLM (e.g. final HTML assembly for compliance-critical output, where consistency matters more than generative flexibility).
Establish how we evaluate agent quality— test prompts, example sets, and a way to tell whether a pr

📌 Ai Engineer Bengaluru
🏢 Accolite Digital India
📍 Bengaluru