Technical Lead AI

Client of Xperts4

Key Responsibilities:
Technical Leadership: Guide the architecture and implementation of AI chatbot solutions leveraging GenAI, Agentic AI, LangGraph, and MCP frameworks.
Team Mentorship: Mentor and develop GenAI engineers, fostering a culture of technical excellence and continuous learning.
Best Practices: Establish and enforce best practices in coding, design, testing, and deployment of AI systems.
Cross-Functional Collaboration: Work closely with data scientists, software engineers, UI/UX designers, and product managers to translate business requirements into technical deliverables.
Quality Assurance: Maintain high standards for code quality, system reliability, and solution scalability through rigorous code reviews and testing protocols.
Project Delivery: Oversee the end-to-end project lifecycle, ensuring timely delivery of milestones and alignment with client objectives.
Client Engagement: Serve as the technical point of contact for clients, providing regular updates, technical guidance, and aligning deliverables with business goals.
Continuous Improvement: Identify opportunities for process optimization and technological innovation within the AI chatbot domain.

Skills :
Data Models and Architecture: Understanding of data modeling, data pipeline design, and scalable data architecture principles.
UI/UX Concepts: Familiarity with user interface and user experience design as it relates to conversational AI applications.
Cloud Deployment: Experience deploying AI solutions on major cloud platforms (e.g., Azure, AWS, GCP).
DevOps Practices: Knowledge of CI/CD pipelines, containerization, and infrastructure automation is a plus.
Generative AI (GenAI) Expertise: Proven track record in designing and developing solutions using GenAI models and architectures.
Agentic AI Experience: Hands-on experience with Agentic AI systems, including multi-agent orchestration and workflow automation.
LangGraph Proficiency: Extensive experience implementing conversational flows and logic with the LangGraph framework.
Model Context Protocol (MCP): Deep understanding and practical application of MCP for managing context and state in large language model deployments.
Python Programming: Advanced proficiency in Python, including experience with AI/ML libraries and frameworks.
Team Management: Demonstrated ability to lead, mentor, and inspire technical teams to achieve high performance and deliver quality outcomes.
Communication: Excellent written and verbal communication skills, with the ability to explain complex technical concepts to both technical and non-technical stakeholders.