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We are looking for a Full Stack AI Engineer. In this role, you will design and implement intelligent systems that drive innovation in conversational AI and autonomous workflows. You will work across the technology stack, leveraging cutting-edge AI frameworks and tools to create scalable, enterprise-grade applications. If you thrive in a dynamic environment and are passionate about advancing AI capabilities, this position offers an excellent opportunity to make a meaningful impact.
Job Responsibility:
Develop end-to-end workflows for AI agents using advanced frameworks, ensuring efficient task orchestration, memory management, and safety measures
Collaborate with cross-functional teams to communicate technical decisions and balance trade-offs for optimal solutions
Utilize AI coding tools to accelerate development while adhering to enterprise standards for security and reliability
Define and implement metrics to evaluate AI agent performance, contributing to the standardization of evaluation methods across products
Design scalable system architectures that optimize caching, streaming, observability, and cost-efficiency
Integrate AI agents with existing services and new microservices to ensure robust and scalable deployments
Conduct thorough code reviews to enhance performance, enforce secure coding practices, and uphold software quality
Build and deploy systems that adhere to best practices for real-time communication, such as WebRTC and WebSockets
Lead efforts in debugging and performance tuning across both front-end and back-end components
Continuously optimize workflows and processes to enhance the reliability and scalability of AI-powered systems
Requirements:
Proficiency in back-end development languages such as TypeScript, JavaScript, and Python, along with experience in modern front-end frameworks like React
Hands-on experience with AI frameworks and SDKs for agent development, including prompt engineering and safety guardrails
Strong knowledge of cloud and infrastructure technologies, including AWS, Azure, Docker, Kubernetes, and infrastructure-as-code tools like Pulumi or Terraform
Familiarity with real-time communication tools such as WebRTC and WebSockets for data streaming
Demonstrated expertise in eval-driven development and creating evaluation metrics for AI systems
Proven ability to debug and optimize performance across all layers of the stack
Exceptional communication skills to explain complex technical concepts and drive consensus among stakeholders
Experience in successfully delivering AI products from prototype to production, including observability and incident resolution