AI engineer

I engineer Location: charlotte, NC Onsite: 3 days a week Status: anything is fine Contract: 6 months-18 months Interview process: 1 and done video (if they are unsure, might have 1 other interview) Must have: AI automation, document ingestion, structuring unstructured data, workflow automation using AI and python. Top Must-Haves: experience building AI-driven automation, especially: Document ingestion Structuring unstructured data Workflow automation using AI Strong Python bility to build solutions and also guide the team - the team does not have much AI experience Project Details: 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