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AuthBridge Linkedin · Posted 2mo ago

AI Engineer (Team Lead)

Gurugram, Haryana, India

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Indexed description

What You Will Do

  • Design and develop AI agents and multi-agent systems for real-world use cases
  • Build end-to-end AI-native products (idea → architecture → development → testing → deployment → production)
  • Use Claude Code / Cursor / Windsurf / similar tools to accelerate development
  • Implement agent orchestration, memory systems, and tool integrations
  • Develop LLM-powered pipelines (RAG, fine-tuning, evaluation frameworks)
  • Integrate APIs, databases, and external systems into workflows
  • Set up AI-native testing frameworks (prompt testing, evaluation pipelines, agent validation)
  • Deploy and monitor systems with focus on latency, cost, and reliability



✅ Must-Have Skills

  • Strong hands-on experience with:
  • Python (mandatory)
  • Backend systems & API design
  • LLM ecosystems (OpenAI, Claude, Gemini, open-source models)
  • Proven experience in:
  • AI agent frameworks (LangChain, CrewAI, AutoGen, Google ADK, AWA AgentCore, or custom agents)
  • RAG pipelines, embeddings, vector databases
  • Prompt engineering & evaluation
  • Critical Requirement:Ability to build end-to-end using Claude Code or similar AI coding tools
  • Experience with “vibe coding” workflows (rapid build → iterate → test → deploy)
  • Proven ability to go from problem statement → working product → deployed system independently
  • Understanding of:
  • Multi-agent architectures
  • Tool/function calling
  • Memory systems
  • Deployment experience:
  • Cloud (AWS / GCP / Azure)
  • Docker (Kubernetes is a plus)



⭐ Good to Have

  • Built or contributed to AI-first products/startups
  • Experience with AI evaluation frameworks (prompt testing, agent scoring, guardrails)
  • Exposure to frontend frameworks (React)
  • Open-source contributions in AI/ML



👤 Who You Are

  • Strong Computer Science background (Tier-1: IITs, IIITs, BITS, or top global universities)
  • Have shipped AI systems end-to-end
  • Thrive in fast, ambiguous environments
  • Strong bias for execution, speed, and ownership
  • Able to leverage AI tools to multiply output, not just assist coding

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