Lead AI Engineer I - R01571042

Job requirements

• Design and develop end-to-end full-stack applications, covering frontend, backend, and API components
• Build and deploy agentic AI applications using multi-agent systems and autonomous workflows
• Implement scalable backend systems with microservices and event-driven architectures to support intelligent solutions
• Develop cloud-native solutions on platforms such as Azure, AWS, and GCP, ensuring robust and scalable deployments
• Integrate LLM-based frameworks and agent orchestration tools to enable adaptive and intelligent workflows
• Enforce best practices in code quality, testing, debugging, observability, performance optimization, security, and scalability
• Collaborate with cross-functional teams to deliver technical solutions aligned with business requirements
• Contribute to design reviews and architectural decisions for AI-driven systems

• Full-stack development with React, Angular, or Vue for frontend
• Backend development using Node.js, Java Spring Boot, or Python frameworks (FastAPI, Django)
• RESTful API design and microservices architecture
• Experience with Azure, AWS, and GCP cloud platforms
• Hands-on experience with Docker containers and Kubernetes
• CI/CD pipeline implementation
• Agentic AI frameworks such as LangChain, LlamaIndex, Semantic Kernel, AutoGen, or CrewAI
• Prompt engineering and Retrieval-Augmented Generation (RAG) techniques

• Familiarity with vector databases such as FAISS or Pinecone
• Knowledge of event streaming systems like Kafka or Pub/Sub
• Experience with federated learning or privacy-preserving algorithms
• Contributions to open-source projects or hackathons in AI/ML or full-stack domains
• Experience with model experimentation platforms such as Domino Datalabs or Databricks ML

• Bachelor's degree in Computer Science, Artificial Intelligence, Data Science, or a closely related discipline
• Certification in cloud platforms (e.g., AWS Certified Machine Learning Specialty, Azure AI Engineer Associate, Google Cloud Professional Machine Learning Engineer)
• Certification in full-stack development or AI frameworks (e.g., Full Stack Web Development, TensorFlow Developer Certificate)