Enterprise GenAI Architect

Role Overview

Design and deploy enterprise-grade AI solutions (LLMs, RAG, agents) by selecting appropriate models, building data pipelines, and integrating them with cloud platforms (AWS, Azure, GCP). Lead technical strategies, ensure scalability, manage AI security/ hallucinations, and bridge business needs with engineering teams.

Key Responsibilities

System Design & Architecture: Architect end-to-end Generative AI systems, including retrieval-augmented generation (RAG) and vector data systems. Model Selection & Tuning: Evaluate and select cutting-edge commercial (e.g., GPT-4) and open-source models, and fine-tune models for domain-specific use cases. LLMOps & Pipelines: Establish LLMOps standards for model versioning, evaluation, prompt management, and CI/CD, ensuring robust, production-grade AI. Integration & Security: Integrate AI solutions with existing APIs, applications, and databases while enforcing security, privacy, and guardrails to manage hallucinations and adversarial attacks. Strategic Leadership: Collaborate with stakeholders to map business challenges to AI solutions and establish AI governance frameworks

Required Skills & Qualifications

Technical Expertise: Deep knowledge of NLP, Python, deep learning frameworks (PyTorch/ TensorFlow), and AI frameworks like LangChain, Autogen, or CrewAI. Cloud & Data Systems: Extensive hands-on experience with AI services on AWS, Azure, or GCP. Expertise in vector databases (e.g., Pinecone, Milvus, Chroma) and embedding techniques. GenAI-Specific Skills: Prompt engineering, RAG architectures, Fine-tuning LLMs, Vector databases. Soft Skills: Problem-solving mindset, strategic thinking, and strong communication (explaining AI to non-technical teams).