Remote AI Architect

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.

Artificial Intelligence (AI), Artificial Intelligence (AI) Programming Languages, Artificial Intelligence (AI), Risk Management, Privacy Controls, Risk Modeling, Computer Science, Software Development, Software Engineering, Data Science, Remote AI Architect needs 10+ years’ experience 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.