Technical Lead

  • $110,000–$130,000 Per Year

Highlights

Cloud exposure preferred (Azure/AKS, managed services), with bonus experience in performance tuning, frontend collaboration, and model governance/monitoring. • Design and develop Python-based APIs and microservices (FastAPI, async patterns) and agentic AI workflows using LangChain/LangGraph.

Numbers & Facts

LocationCary, NC
Salary$110,000–$130,000 Per Year

Description

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.
  • • 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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Salary Range: $110,000 to $130,000 per year

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