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# AI Solution Lead in Cary, NC (Fulltime, Onsite)

- **Company:** Northern Base
- **Location:** Clarks Summit, PA, United States
- **Experience:** Expert
- **Salary:** $62,400.0 - $72,800.0
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Microsoft Azure, Cloud Computing, Continuous Integration, Information Engineering, Software Debugging, Event-Driven Programming, Python (Programming Language), Performance Tuning, Search Technologies, Delivery Pipeline, Large Language Models, Concurrency, Caching, Backend, Rate Limiting, Fastapi, Kubernetes, Front End Software Development, Software Version Control, Microservices
- **Published:** August 1, 2026
- **Apply:** https://www.careerjet.com/jobad/us5ed9ee87d93dc64fb7e2f54288acbdf0

## About the Role

Must Have Technical/Functional Skills
13+ years of experience with IT
Build and productionize cloud native backend services and AI/LLM inference pipelines.
Design and develop Python-based APIs and microservices (FastAPI, async patterns) and agentic AI workflows using LangChain/LangGraph.
Implement and optimize LLM capabilities including embeddings, RAG, vector search, prompt/context engineering, and model versioning.

## Description

Package, serve, and monitor models for real-time and batch inference, ensuring operational readiness and performance.
Build event driven, resilient integrations and containerized services, with hands-on Kubernetes debugging and Helm-based deployments.
Establish observability, SLOs, CI/CD automation, testing
Apply strong systems design principles (concurrency, caching, reliability, rate limiting) and robust data engineering practices.
Cloud exposure preferred (Azure/AKS, managed services), with bonus experience in performance tuning, frontend collaboration, and model governance/monitoring.
Roles & Responsibilities
Build and productionize cloud native backend services and AI/LLM inference pipelines.
Design and develop Python-based APIs and microservices (FastAPI, async patterns) and agentic AI workflows using LangChain/LangGraph.
Implement and optimize LLM capabilities including embeddings, RAG, vector search, prompt/context engineering, and model versioning.
Package, serve, and monitor models for real-time and batch inference, ensuring operational readiness and performance.
Build event driven, resilient integrations and containerized services, with hands-on Kubernetes debugging and Helm-based deployments.
Establish observability, SLOs, CI/CD automation, testing
Apply strong systems design principles (concurrency, caching, reliability, rate limiting) and robust data engineering practices.
Cloud exposure preferred (Azure/AKS, managed services), with bonus experience in performance tuning, frontend collaboration, and model governance/monitoring.


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