Technical Lead AI

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.

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.