Technical Lead – AI
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About the Role
The Technical Lead – AI is responsible for spearheading the design, development, and deployment of AI-driven chatbot solutions. The role requires deep expertise in Generative AI, Agentic AI, LangGraph, and Model Context Protocol frameworks.
Key Skills for This Role
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Overview
The Technical Lead – AI is responsible for spearheading the design, development, and deployment of AI-driven chatbot solutions.
The role requires deep expertise in Generative AI, Agentic AI, LangGraph, and Model Context Protocol frameworks.
The Technical Lead will provide technical direction, mentor the GenAI team, and ensure the delivery of robust, scalable, and innovative chatbot solutions for enterprise clients.
Responsibilities: Guide the architecture and implementation of AI chatbot solutions leveraging Generative AI, Agentic AI, LangGraph, and Model Context Protocol frameworks.
Mentor and develop GenAI engineers, fostering a culture of technical excellence and continuous learning.
Establish and enforce best practices in coding, design, testing, and deployment of AI systems.
Work closely with data scientists, software engineers, UI/UX designers, and product managers to translate business requirements into technical deliverables.
Maintain high standards for code quality, system reliability, and solution scalability through rigorous code reviews and testing protocols.
Oversee the end-to-end project lifecycle, ensuring timely delivery of milestones and alignment with client objectives.
Serve as the technical point of contact for clients, providing regular updates, technical guidance, and aligning deliverables with business goals.
Identify opportunities for process optimization and technological innovation within the AI chatbot domain.
Requirements Must-Have Skills Generative AI expertise, with a proven track record in designing and developing solutions using GenAI models and architectures.
Hands-on experience with Agentic AI systems, including multi-agent orchestration and workflow automation.
Extensive experience implementing conversational flows and logic with the LangGraph framework.
Deep understanding and practical application of Model Context Protocol for managing context and state in large language model deployments.
Advanced proficiency in Python, including experience with AI/ML libraries and frameworks.
Demonstrated ability to lead, mentor, and inspire technical teams to achieve high performance and deliver quality outcomes.
Excellent written and verbal communication skills, with the ability to explain complex technical concepts to both technical and non-technical stakeholders.
Good-to-Have Skills Understanding of data modeling, data pipeline design, and scalable data architecture principles.
Familiarity with user interface and user experience design as it relates to conversational AI applications.
Experience deploying AI solutions on major cloud platforms such as Azure, AWS, or GCP.
Knowledge of CI/CD pipelines, containerization, and infrastructure automation is a plus.
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