Job Title: AI Engineer / Architect (Agentic Systems)

About the Role: We are seeking a highly skilled Senior AI Engineer to design and implement next-generation autonomous AI systems.

In this role, you will architect complex, multi-agent workflows where specialized AI personas collaborate, self-correct, and integrate with human stakeholders to solve complex enterprise problems.

If you have deep experience moving beyond simple chat prompts into cyclic, stateful agent orchestration and automated code generation, we want to hear from you.

Key Responsibilities

Design and deploy enterprise-grade multi-agent architectures using stateful, cyclic frameworks (e.g., LangGraph, AutoGen, CrewAI).

Build advanced Human-in-the-Loop (HITL) workflows that allow AI agents to dynamically pause, solicit user feedback, process structured inputs, and resume execution.

Develop robust text-to-code and code-generation pipelines, ensuring output accuracy through automated verification and testing layers. Implement structured data parsing (JSON formatting, Pydantic validation) to guarantee reliable communication interfaces between different AI agents.

Optimize context windows and implement advanced Retrieval-Augmented Generation (RAG) strategies to manage long-term state across complex workflows.

Establish secure,



sandboxed execution environments (e.g., Docker) for automated testing, evaluation, and iteration of AI-generated outputs.

Required Technical Skills & Qualifications

Core Languages: Deep proficiency in Python and solid experience with up-to-date backend architecture.

Agentic Frameworks: Proven experience building with LangGraph (strongly preferred for cyclic state management), CrewAI, or AutoGen.

LLM Expertise: Advanced prompt engineering, system prompt design, and experience leveraging leading models (e.g., Claude Sonnet, GPT-4o) via APIs for complex reasoning tasks.

State & Data Management: Experience managing state across asynchronous processes, building memory systems for AI, and ensuring strict JSON schema adherence.

Software Fundamentals: Strong understanding of Git workflows, CI/CD pipelines, containerization (Docker), and safe code-execution practices.

Problem Solving: Ability to build error-handling, logging, and self-correction loops into AI chains to prevent compounding errors.

Nice-to-Haves Experience with semantic caching and vector databases (e.g., Pinecone, Chroma, Qdrant).

Contributions to open-source LLM orchestration or multi-agent frameworks. Experience in automated software testing or compilers.

📌 Gen AI Engineer (Dehradun)
🏢 Cynet Systems
📍 Dehradun