AI/MLTech Lead/Architect---Raleigh, NC (Hybrid)

[roy garg]

Position:- AI/MLTech Lead/Architect

Location: Raleigh, NC (Hybrid)

Experience Level: 10-15 Years

USC & H4 Only

Required Skills-  Claude, Vive Coding, Data Scientist, MCP, Agentic AI, LLM understanding, RAG. Architecture end to end, built systems.

 We are seeking a highly skilled AI/ML Leader with a strong foundation in Python and microservices architecture, capable of bridging the gap between traditional backend systems and modern AI/ML platforms. The ideal candidate will have experience or a strong interest in LLM-based frameworks (e.g., LangChain, Agentic AI), and be capable of designing scalable, intelligent solutions that integrate with major AI platforms.

 

Key Responsibilities:

 

·        Architect and design scalable, secure, and high-performance microservices using Python.

·        Collaborate with AI/ML teams to integrate LLM-based tools and frameworks into enterprise applications.

·        Understand and work with MCP (Model Context Protocol), A2A (Agent-to-Agent) communication, and LLM orchestration frameworks like LangChain and Agentic AI.

·        Evaluate and recommend AI platforms and tools for enterprise use cases.

·        Translate business requirements into technical solutions that leverage both traditional and AI-driven components.

·        Lead technical discussions with stakeholders, including product managers, data scientists, and platform teams.

·        Ensure architectural alignment with enterprise standards and best practices.

  Required Skills & Experience:

 

·        5+ years of experience in backend development with Python.

·        Proven experience designing and deploying microservices architectures.

·        Familiarity with AI/ML concepts, especially LLMs, prompt engineering, and AI agents.

·        Understanding of LangChain, Agentic AI, or similar LLM orchestration frameworks.

·        Experience integrating with AI platforms (e.g., OpenAI, Azure OpenAI, Anthropic, Hugging Face).

·        Strong understanding of API design, event-driven systems, and cloud-native architectures.

·        Excellent communication and stakeholder management skills.

                      Production-level RAG implementation experience.

·        Hands-on experience with LLM-based applications or AI agent frameworks.

·        Exposure to MCP, A2A, or similar AI infrastructure concepts.

·        Experience with containerization (Docker, Kubernetes) and CI/CD pipelines.

·        Knowledge of data pipelines and AI model lifecycle management

Thank You

Rahul 

ra...@ccube.com