Lead AI Engineer

About the Role

We are seeking an experienced Lead Developer to join our dynamic team focused on building cutting-edge Agentic AI solutions. In this role, you will lead the development of a sophisticated multi-agent system leveraging technologies such as LangGraph, Quadrant, and Dgraph. The ideal candidate has 10+ years of Python development experience and a strong background in designing, developing, and deploying AI-driven platforms. You will play a key leadership role in shaping system architecture, driving innovation, and ensuring high-quality, scalable solutions.

Key Responsibilities

• Multi-Agent System Development
  Lead the design, development, and deployment of a multi-agent AI platform using LangGraph, Quadrant, and Dgraph.
• Scalable Agentic AI Solutions
  Build scalable, high-performance systems with a strong focus on agentic AI and seamless integration with knowledge graph technologies.
• Cross-Functional Collaboration
  Work closely with cross-functional teams to ensure smooth integration of AI agents with cloud-based infrastructures.
• Codebase Ownership
  Take ownership of the codebase, ensuring high standards of quality, maintainability, and scalability.
• Cloud-Native Development
  Utilize Cloud Code and cloud-native best practices to improve reliability, efficiency, and developer productivity.
• Innovation & Research
  Explore emerging frameworks, tools, and methodologies in AI and machine learning to drive continuous innovation.
• Containerized Deployment
  Manage and orchestrate containerized AI applications using Docker and Kubernetes.
• LLM Optimization
  Implement and optimize AI models using VLLM for large-scale language model inference and fine-tuning.

Basic Qualifications

• 10+ years of Python development experience, with a focus on AI, machine learning, or complex systems integration.
• Proven experience building multi-agent systems and knowledge graph–based architectures, particularly with LangGraph, Quadrant, and Dgraph.
• Strong proficiency with Cloud Code and cloud-native development practices.
• Hands-on experience with Docker and Kubernetes for containerized AI workloads.
• Experience working with large-scale language models, especially using VLLM for inference and fine-tuning.
• Excellent problem-solving abilities, leadership skills, and experience mentoring junior engineers.

Preferred Qualifications

• Experience designing and deploying distributed systems on cloud platforms such as AWS, GCP, or Azure.
• Familiarity with reinforcement learning, neural networks, and advanced AI algorithms.