AI Solution Architect

Roles and Responsibilities

• Architect and develop end-to-end technical solutions for the EisnerAI Platform across AI, agentic, data management, and governance layers.
• Build and deploy enterprise AI agent frameworks focusing on initiation, execution state management, logging, and automated error handling.
• Design model evaluation frameworks and drive post-training optimization and fine-tuning for custom LLMs and autonomous AI agents.
• Implement scalable vector search, semantic search, and knowledge mining pipelines using Azure AI Search.
• Integrate enterprise data engineering workflows and machine learning model pipelines using Azure Databricks.
• Ensure strict compliance with corporate data governance policies, security standards, and responsible AI practices.
• Collaborate with cross-functional technical and business teams to translate complex requirements into resilient cloud-native AI architectures.

Preferred Candidate Profile

• Experience: 8+ years of total software development experience with at least 4+ years dedicated to AI/ML architecture roles, and 3+ years of hands-on Azure cloud experience.
• Education: Bachelor degree in Computer Science, Engineering, Data Science, or related field (Master degree in CS, AI/ML, or MBA with technical focus preferred).
• Technical Expertise: Strong hands-on background in Microsoft Azure AI Foundry, Azure OpenAI Service, prompt engineering, fine-tuning, Azure AI Search, and Azure Databricks.
• Core Coding Skills: High proficiency in Python along with C#, JavaScript/TypeScript, or SQL.
• Preferred Exposure: Azure Machine Learning, MLOps (Docker, Kubernetes, Azure DevOps, GitHub Actions), vector databases (Azure Cosmos DB, Pinecone, Weaviate), and frameworks like PyTorch, TensorFlow, or Hugging Face.
• Assumed Requirements: Prior experience delivering enterprise solutions within financial, accounting, or professional services domains