Agentic AI Data Architect

Job Description

Job Description

Position: Agentic AI Data Architect

Location: Remote

Duration: 6 Months Contract

Job Summary:

Update: one thing the client is looking for is Azure Data Factory experience for this AI Data Architect. this is a competitive backfill for us and we can win this fairly easily with some solid folks. Top 3 skills we're looking for are: 1. Agentic AI Architecture at an Enterprise level 2. Data Architecture/Engineering exposure. 3. Azure Data Factory (Azure is the environment)

Position Summary:

We are seeking an experienced Agentic AI Data Architect to design, build, and deploy AI-powered applications that automate business processes and improve decision-making. This is a hands-on technical leadership role focused on developing production-ready AI solutions using large language models (LLMs), agentic AI frameworks, retrieval-augmented generation (RAG), and modern cloud data platforms.

The ideal candidate will have a strong background in AI application development, data architecture, and software engineering, along with the ability to guide teams and establish best practices for enterprise AI adoption.

Key Responsibilities AI Solution Design & Development:

• Design and build agentic AI applications that automate business workflows and improve operational efficiency.
• Develop AI solutions using LLMs, multi-agent architectures, and retrieval-augmented generation (RAG).
• Create and optimize prompt engineering strategies, reasoning workflows, and AI integrations.
• Build and maintain APIs, backend services, and AI application components.
• Evaluate new AI technologies and recommend solutions that drive business value. Data Architecture & Integration
• Design data retrieval and grounding strategies that connect AI applications to enterprise data sources.
• Develop vector search, embedding, and knowledge retrieval solutions.
• Partner with data engineering teams to ensure AI systems leverage reliable and secure data pipelines.
• Establish standards for AI performance, accuracy, and reliability. Technical Leadership
• Serve as a technical lead for AI architecture and development initiatives.
• Provide guidance and mentorship to developers, engineers, and technical teams.
• Review solution designs and help establish AI development standards and best practices.
• Translate complex AI concepts into practical solutions for technical and business stakeholders. Platform & Automation
• Support and enhance enterprise AI platforms and integrations.
• Implement automation, CI/CD, and Infrastructure-as-Code (IaC) practices for AI deployments.
• Maintain technical documentation, architecture diagrams, and implementation standards. Required Qualifications
• 8 years of experience in software engineering, data engineering, AI/ML, or related technical fields.
• 2 years of hands-on experience building and deploying AI or LLM-based applications.
• Strong experience with OpenAI, Claude, or similar LLM platforms and APIs.
• Experience building agentic AI solutions and multi-agent workflows.
• Strong understanding of prompt engineering, tool usage, and AI application design.
• Experience designing and implementing RAG solutions and vector search technologies.
• Proficiency in Python and API development.
• Experience working with cloud platforms and modern data architectures.
• Strong communication, leadership, and problem-solving skills. Preferred Qualifications
• Experience with MCP (Model Context Protocol) and agent orchestration frameworks such as LangGraph, LangChain, CrewAI, or AutoGen.
• Experience with Databricks, Snowflake, Azure AI services, or similar platforms.
• Background in workflow automation, analytics, pricing, sales, or customer-facing business applications.
• Experience working in regulated enterprise environments.
• Advanced degree in Computer Science, Data Science, AI, or a related field.

** Technical Environment Experience with the following technologies is highly desirable:**

• OpenAI, Azure OpenAI, Claude
• MCP (Model Context Protocol)
• Python, FastAPI
• LangChain, LangGraph, CrewAI
• Databricks, Delta Lake
• Vector databases and semantic search platforms
• Azure, AWS, or other cloud environments
• CI/CD pipelines and Infrastructure as Code (Terraform, GitHub Actions, Azure DevOps)

Success in This Role Success will be measured by:

• Delivery of scalable, production-ready AI applications.
• Ability to move AI use cases from concept to deployment.
• Technical leadership and mentorship of engineering teams.
• Quality, reliability, and business impact of AI solutions.
• Effective collaboration with engineering, data, and business stakeholders