In this role, youll manage a high-performing backend team responsible for building scalable microservices and cloud-based infrastructure, while integrating advanced AI capabilities such as evaluation frameworks, RAG pipelines, knowledge base management, embeddings, LLMs, chatbot assistants, and agent orchestration.
What Youll Do:
Lead and mentor a backend engineering team developing a scalable, cloud-native AI platform. Youll foster a culture of technical excellence, collaboration, and ownership while ensuring delivery of robust, production-grade systems.
Define the technical direction and architecture for backend services, focusing on microservices, distributed systems, and AWS-based infrastructure to support AI-driven applications.
Integrate cutting-edge AI capabilities into the platform, including RAG pipelines, embeddings, LLM orchestration, and evaluation frameworks, ensuring performance, scalability, and security.
Collaborate with product and engineering teams to translate business needs into backend solutions that enable AI-agent use cases and accelerate innovation across the organization.
Drive agile processes and best practices, leveraging tools like Jira to manage sprints, track progress, and continuously improve team efficiency and delivery quality.
Scale the team strategically, hiring and onboarding top talent while mentoring future leaders to support the growth of the Gen AI Platform group.
Youll Do It Using:
7+ years of experience in software R&D, including 2+ years in a leadership role managing backend engineering teams.
Strong backend development expertise, including microservices architecture, distributed systems, and cloud-native design on AWS.
Proficiency in Python for building scalable backend services and integrating AI components.
Hands-on experience with Generative AI technologies, including LLMs, RAG pipelines, embeddings, and agent frameworks.
Familiarity with AI development ecosystems, such as AWS Bedrock, LangChain, LangGraph, OpenAI APIs, and vector databases.
Experience with agile methodologies and tools (e.g., Jira) to manage team workflows and deliver high-quality software in fast-paced environments.
Advantages:
Familiarity with Machine Learning or Data Science concepts, enabling better collaboration with AI-focused teams.
Prior experience in AI/ML infrastructure, MLOps, or agent orchestration.
Exposure to data engineering, security, or AI ethics considerations in product development.
