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Job Description & How to Apply Below
Overview
Build and scale a pro‑code autonomous agent ecosystem using modern open‑source AI technologies across agent orchestration, LLMOps, AI safety, inference, Kubernetes, and full‑stack user interfaces.
Key Responsibilities- Design and build complex stateful multi‑agent systems using Lang Graph, CrewAI, or Auto Gen.
- Develop advanced agentic RAG pipelines using Llama Index, Haystack, or similar frameworks.
- Implement custom tool‑calling logic and Model Context Protocol integrations, connecting AI agents with internal databases, APIs, and enterprise systems.
- Architect a robust AI safety layer using Guardrails AI, NVIDIA NeMo Guardrails, or Meta Llama Guard, alongside real‑time monitoring and observability for LLM inputs and outputs.
- Deploy and optimise open‑source models such as Llama, Mistral, or Qwen using high‑performance inference tools like vLLM, Hugging Face TGI, Ollama, or Seldon Core.
- Build scalable AI infrastructure using Docker, Kubernetes, Terraform, Pulumi, and related tooling.
- Contribute to user‑facing interfaces using React, Type Script, and streaming‑first UI patterns.
- 6+ years of software engineering experience, 2+ years of hands‑on generative AI experience.
- Strong Python skills including asyncio and Pydantic.
- Type Script/Node.js experience.
- Experience with Lang Graph, CrewAI, Auto Gen, or similar agentic frameworks.
- Knowledge of Agentic RAG, Llama Index, Haystack, or similar tools.
- Experience with open‑source model deployment and serving tools such as vLLM, TGI, Ollama, or Seldon Core.
- Strong understanding of AI safety guardrails, observability, and governance.
- Experience with vector databases such as Milvus, Weaviate, Qdrant, or ChromaDB.
- Strong Docker, Kubernetes, and cloud‑native infrastructure experience.
- React experience for building internal tools, dashboards, or AI interfaces.
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