Role: AI engineer
Location: charlotte, NC (Hybrid)
Contract: 6 months-18 months
Interview process: 1 and done video
Must have: AI automation, document ingestion, structuring unstructured data, workflow automation using AI and python.
o Top Must-Haves:
• experience building AI-driven automation, especially:
• Document ingestion
• Structuring unstructured data
• Workflow automation using AI
• Strong Python
• Ability to build solutions and also guide the team – the team does not have much AI experience
•
o Project Details:
• A hands-on AI engineer who can quickly build LLM-powered automation solutions for messy enterprise data and guide a team doing it
• Build AI-driven automation to streamline and standardize data intake into enterprise reporting tools.
• Automate ingestion of incoming data
• Structure/normalize unstructured inputs
The Senior AI Engineer will design and deliver enterprise-grade AI agent solutions that automate document ingestion, orchestrate workflows, and augment decision-making across risk, finance, and governance domains. This role focuses on leveraging and integrating existing LLM platforms to build scalable, production-ready agent systems—embedding AI into engineering and product delivery workflows without developing foundational models. The role requires strong system design, agent orchestration, and pragmatic application of AI to drive measurable operational efficiency.
Required Qualifications
Experience in software engineering, AI solution engineering, or applied data workflows (
Proven experience building AI agents or agentic workflows using existing LLMs (task orchestration, tool use, memory, workflow chaining)
Hands-on experience with AI-assisted development tooling (e.g., GitHub Copilot, Copilot Studio, or equivalent) to accelerate engineering productivity and solution design
Strong understanding of RAG patterns, document ingestion, and unstructured data processing
Experience designing end-to-end AI workflows (prompting, retrieval, tool integration, output validation)
Proficiency in prompt engineering, grounding, and guardrails to ensure reliability and control
Strong Python engineering skills and experience building production-grade services and APIs
Experience with cloud-native architectures and integration into enterprise environments and tooling
Ability to define and measure outcomes (efficiency gains, cost reduction, adoption metrics)
Strong stakeholder engagement skills and ability to translate business problems into pragmatic AI solutions
Desired Qualifications
Experience designing multi-agent or orchestrated agent systems for enterprise workflows
Experience building interactive copilots or embedded AI assistants within engineering or operational tools
Familiarity with agent evaluation techniques, human-in-the-loop validation, and iterative improvement loops
Experience applying AI to workflow automation, operational efficiency, or service delivery transformation
Knowledge of Responsible AI, governance, and enterprise risk considerations
Track record of driving AI adoption across engineering or product organizations
Experience operating in product-centric, outcome-driven delivery models
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