AI & ML Tech Lead/Architect

Position:- AI & ML Tech Lead
Location: Raleigh, NC (Hybrid)
Job Type: Full Time/W2 Only

Exp Level- 8-15 Years
 
Required Skills- Claude, Vibe Coding, Data Scientist, MCP (Model Context Protocol), 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, who can bridge 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 Protoco), 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
Nice-to-Have / Big Plus:

• Experience with MCP, A2A, or similar AI infrastructure concepts
• Knowledge of data pipelines & AI model lifecycle management
• Prior experience architecting enterprise AI platforms

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