Agentic AI Architect

CYNET SYSTEMS INC.
Charlotte, United States of America
yesterday

Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Working hours
Regular working hours
Languages
English
Experience level
Senior

Job location

Charlotte, United States of America

Tech stack

API
Artificial Intelligence
Amazon Web Services (AWS)
User Authentication
Azure
Software Quality
Data Governance
Distributed Systems
Python
OAuth
Search Technologies
Software Engineering
Google Cloud Platform
Enterprise Software Applications
Large Language Models
Multi-Agent Systems
Prompt Engineering
iOS
Generative AI
Backend
Event Driven Architecture
Data Management
Machine Learning Operations
Front End Software Development
Api Design
Microservices

Job description

  • Define end-to-end architecture for agentic AI solutions, including multi-agent systems, orchestration layers, and integrations.
  • Design and implement scalable agent frameworks using technologies such as LangChain, Semantic Kernel, AutoGen, CrewAI, etc.
  • Lead development of LLM-driven systems (Azure OpenAI, OpenAI, Anthropic, etc.) with robust prompting strategies.
  • Architect RAG pipelines, memory systems, and tool integration layers.
  • Establish best practices for AI governance, security, and Responsible AI.
  • Design integration strategies with enterprise systems (CRM, ERP, APIs, data platforms).
  • Provide technical leadership, mentorship, and code quality oversight to development teams.
  • Collaborate with business stakeholders to translate requirements into technical solutions.
  • Ensure performance, scalability, reliability, and cost optimization of AI systems.
  • Drive innovation by evaluating emerging AI technologies and frameworks.

Requirements

  • 10+ years of experience in software engineering, AI/ML, or solution architecture.
  • Strong expertise in Python and modern backend architectures.
  • Hands-on experience with LLM ecosystems and agent frameworks.
  • Proven experience designing multi-agent systems and distributed architectures.
  • Deep understanding of prompt engineering and LLM optimization.
  • Deep understanding of embeddings and vector databases (FAISS, Pinecone, Azure AI Search, etc.).
  • Deep understanding of Retrieval-Augmented Generation (RAG).
  • Experience with cloud platforms (Azure preferred, AWS/Google Cloud Platform acceptable).
  • Strong knowledge of API design, microservices, and event-driven architecture.
  • Familiarity with security, authentication (OAuth, SSO), and data governance.
  • Experience leading technical teams and driving architecture decisions., * Experience with Model Context Protocol (MCP) or similar integration standards.
  • Exposure to enterprise AI transformation programs.
  • Knowledge of MLOps / LLMOps frameworks.
  • Experience with frontend or conversational UI frameworks.
  • Certifications (Azure AI Engineer, Solutions Architect, etc.).

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