Remote AI Architect

Remote AI Architect with extensive experience in enterprise-wide AI programs, platform buildouts, and AI/ML engineering. Responsible for architectural oversight, technology evaluation, and team mentorship.

Location: Remote

Responsibilities

• Provide architectural oversight across AI/ML projects to ensure consistency, performance, and maintainability.
• Evaluate and select AI technologies, frameworks, cloud services, vector databases, LLM orchestration frameworks, and tooling.
• Support development teams on model selection, training pipelines, prompt engineering, fine tuning, RAG (Retrieval-Augmented Generation), and evaluation methodologies.
• Mentor engineers, analysts, and product teams on AI best practices.

Requirements

• 10+ years' experience in enterprise-wide AI programs or platform buildouts.
• Strong understanding of data governance, privacy, security, and model risk management.
• Prior experience with large-scale transformation programs.
• Bachelor's degree in Computer Science, Engineering, or a related technical field.
• 5+ years of experience in application development, engineering, or solution delivery roles.
• 1+ years of hands-on experience in AI/ML engineering, data science, or AI solution architecture.
• Strong hands-on experience with machine learning frameworks and LLM platforms (e.g., OpenAI, Azure AI Foundry, Copilot Studio/Agent Builder, or comparable generative AI ecosystems).
• Deep expertise in cloud platforms, particularly Microsoft Azure, and modern architectural patterns (microservices, event-driven architectures, API-first design).
• Proficiency in one or more of the following: Python, Azure Machine Learning, or related AI/ML tooling.
• Experience with MLOps/LLMOps ecosystems, including tools such as MLflow, Kubernetes, LangChain, vector databases, and feature stores.
• Strong hands on experience with ML frameworks, LLM platforms - OpenAI, MSFT/Azure Cloud foundry, Copilot Studio Agent builder, low code/no code platforms, and generative AI tools.
• Background in RAG systems, model fine tuning, embeddings, vector storage, and retrieval optimization.