Senior AI / GenAI Solutions Engineer (Python, LangGraph & Agentic Workflows)

Innova Solutions•Charlotte, NC

3d•Hybrid

About The Position

Innova Solutions is immediately hiring for a Senior AI / GenAI Solutions Engineer with expertise in Python, LangGraph, and Agentic Workflows. This is a full-time, contract position for 19 months, located in Charlotte, NC, with a hybrid work model. The role involves developing and deploying multi-agent systems and intelligent workflows using Python, LangGraph, and LangChain for complex tasks. You will also build and scale end-to-end document AI solutions, implement RAG architectures, and integrate AI components with various systems. A key aspect of this role is owning the technical lifecycle of AI solutions, making architectural decisions, and adhering to enterprise AI governance standards. The goal is to translate manual operational workflows into scalable, automated solutions that improve efficiency and reduce errors.

Requirements

Experience in Artificial Intelligence (AI), Machine Learning (ML), Generative AI, or Intelligent Automation solutions.
Hands-on experience with Python and AI development frameworks, including LangChain, LangGraph, Google ADK, and agent-based architectures.
Experience implementing Document AI / Intelligent Document Processing (IDP) solutions involving OCR, PDF parsing, data extraction, confidence scoring, rule-based validation, semantic matching, fuzzy comparison, and API integration.
Strong experience building and deploying LLM-powered applications, AI agents, RAG (Retrieval-Augmented Generation) solutions, and workflow automation platforms.
Must be able to be engaged on a W-2 basis. 1099 contractors and Corp-to-Corp arrangements will not be considered.

Nice To Haves

Experience working in financial services, banking, or complex enterprise environments with rigorous operational and compliance.
Experience integrating GenAI workflows with compliance, KYC (Know Your Customer), or enterprise ticketing systems.
Strong background in containerized deployments (Docker, Kubernetes) and cloud-native architectures.

Responsibilities

Develop and deploy multi-agent systems and intelligent workflows using Python, LangGraph, and LangChain to handle complex intent classification, data collection, and automated task execution.
Build and scale end-to-end document AI solutions involving advanced OCR, PDF parsing, data extraction, confidence scoring, rule-based validation, semantic matching, fuzzy comparison, and API integration.
Implement robust Retrieval-Augmented Generation (RAG) architectures and LLM-powered applications optimized for high-accuracy enterprise data retrieval and verification.
Integrate AI components with REST APIs, Kafka messaging streams, and MongoDB data layers to seamlessly interface with core banking, customer verification, and KYC processes.
Own the technical lifecycle of AI solutions—making and defending architectural decisions (model selection, vector database strategies, orchestration frameworks) while operating under strict enterprise AI governance standards.
Translate manual operational workflows into scalable, automated solutions that reduce handling time, eliminate rework, and enhance end-to-end processing efficiency.