GenAI Engineer

• AI Agent Development: Build and orchestrate AI agents using frameworks like LangChain, AutoGen, or CrewAI, implementing self-healing workflows (e.g., Act-Verify-Refine loops). LLM Integration & Backend: Develop robust backend systems using Python and TypeScript, integrating LLMs into microservices architectures. Data Management for LLMs: Utilize vector databases (Pinecone, Milvus, Weaviate) for agent memory and architect Retrieval-Augmented Generation (RAG) pipelines to enhance LLM accuracy and contextual understanding. Prompt Engineering: Design and optimize prompt strategies, including automated evaluation frameworks, for high-quality LLM output. Context Engineering: Manage LLM information ecosystems, including system prompts, RAG implementation, and conversation history. MLOps & Deployment: Oversee the end-to-end lifecycle of generative models, focusing on inference speed, cost-efficiency, and scalability on cloud platforms (AWS, Google Cloud Platform, Azure). AI Ethics & Compliance: Ensure adherence to security standards, IP regulations, and safety guidelines for all generative models. Tool Orchestration: Define and manage the API/tool access for AI agents to optimize accuracy.

• Technical Proficiency: Strong command of Python, PyTorch, TensorFlow, and Hugging Face libraries. GenAI Experience: Hands-on experience with LangChain, LlamaIndex, vector databases, and fine-tuning techniques (LoRA, QLoRA). API & Backend: Proven ability to integrate AI models into web applications via APIs (OpenAI, Anthropic). Software Engineering: Solid understanding of software engineering best practices, including Git, CI/CD, and Docker.

Preferred Skills:

• Experience with multimodal AI models (image, video, audio generation). Published AI/LLM research or contributions to open-source AI projects. Background in AI governance or safety policy development.

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