Enterprise Architect

As an AI Solutions Architect, your role involves designing and deploying enterprise-grade AI solutions such as LLMs, RAG, and agents. This includes selecting appropriate models, building data pipelines, and integrating them with cloud platforms like AWS, Azure, and GCP. You will lead technical strategies, ensure scalability, manage AI security, and bridge business needs with engineering teams. Your key responsibilities will include: - Architecting end-to-end Generative AI systems, including retrieval-augmented generation (RAG) and vector data systems. - Evaluating and selecting cutting-edge commercial and open-source models, such as GPT-4, and fine-tuning models for domain-specific use cases. - Establishing LLMOps standards for model versioning, evaluation, prompt management, and CI/CD to ensure robust, production-grade AI. - Integrating AI solutions with existing APIs, applications, and databases while enforcing security, privacy, and guardrails to manage hallucinations and adversarial attacks. - Collaborating with stakeholders to map business challenges to AI solutions and establish AI governance frameworks. The required skills and qualifications for this role include: - Deep knowledge of NLP, Python, deep learning frameworks (PyTorch/TensorFlow), and AI frameworks like LangChain, Autogen, or CrewAI. - Extensive hands-on experience with AI services on AWS, Azure, or GCP, expertise in vector databases (e.g., Pinecone, Milvus, Chroma), and embedding techniques. - Specific skills related to prompt engineering, RAG architectures, fine-tuning LLMs, and vector databases. - Soft skills such as a problem-solving mindset, strategic thinking, and strong communication to explain AI concepts to non-technical teams. As an AI Solutions Architect, your role involves designing and deploying enterprise-grade AI solutions such as LLMs, RAG, and agents. This includes selecting appropriate models, building data pipelines, and integrating them with cloud platforms like AWS, Azure, and GCP. You will lead technical strategies, ensure scalability, manage AI security, and bridge business needs with engineering teams. Your key responsibilities will include: - Architecting end-to-end Generative AI systems, including retrieval-augmented generation (RAG) and vector data systems. - Evaluating and selecting cutting-edge commercial and open-source models, such as GPT-4, and fine-tuning models for domain-specific use cases. - Establishing LLMOps standards for model versioning, evaluation, prompt management, and CI/CD to ensure robust, production-grade AI. - Integrating AI solutions with existing APIs, applications, and databases while enforcing security, privacy, and guardrails to manage hallucinations and adversarial attacks. - Collaborating with stakeholders to map business challenges to AI solutions and establish AI governance frameworks. The required skills and qualifications for this role include: - Deep knowledge of NLP, Python, deep learning frameworks (PyTorch/TensorFlow), and AI frameworks like LangChain, Autogen, or CrewAI. - Extensive hands-on experience with AI services on AWS, Azure, or GCP, expertise in vector databases (e.g., Pinecone, Milvus, Chroma), and embedding techniques. - Specific skills related to prompt engineering, RAG architectures, fine-tuning LLMs, and vector databases. - Soft skills such as a problem-solving mindset, strategic thinking, and strong communication to explain AI concepts to non-technical teams.