Join an innovative technology environment focused on designing and delivering advanced AI solutions that solve real-world business challenges. As a Staff Engineer / AI Builder, you will take technical ownership of the architecture, development, and deployment of production-grade AI and machine learning systems. You will lead the development of agentic workflows, generative AI applications, and scalable infrastructure, ensuring reliability, security, and performance. Working across backend, frontend, and cloud environments, you will transform complex technical requirements into practical solutions that create measurable value for clients. You will also play a key role in establishing engineering standards, mentoring colleagues, and influencing architectural decisions. This is an exciting opportunity to shape the future of enterprise AI through hands-on engineering, technical leadership, and close collaboration with multidisciplinary teams.
Agentic AI and Generative AI Engineering:
Design, develop, and maintain sophisticated agentic workflows, including reasoning loops, tool and function calling, and orchestration across single-agent and multi-agent architectures.
Select appropriate architectural patterns based on business requirements, technical complexity, scalability, and performance considerations.
Build and optimize Retrieval-Augmented Generation (RAG) pipelines, including document chunking, embeddings, vector search, re-ranking, and knowledge source refresh mechanisms.
Develop and integrate AI solutions using AWS Bedrock and Agent Core, including Model Context Protocol (MCP)-based tools and integrations.
Design and maintain production-ready system prompts with structured instructions, output constraints, and few-shot examples.
Implement comprehensive evaluation and observability frameworks using golden datasets, RAGAS-style metrics, LLM-as-judge techniques, and tracing tools such as LangFuse or LangSmith.
Build resilient AI systems capable of handling model failures and service degradation through retries, backoff strategies, circuit breakers, fallback models, and appropriate error handling.
Full-Stack and Platform Engineering:
Develop scalable backend services and RESTful APIs using Python and Node.js, including serverless solutions built with AWS Lambda and API Gateway.
Design and manage DynamoDB single-table data models, including composite keys and transactional operations for conversation state, agent memory, and session history.
Implement event-driven architectures and asynchronous workflows using AWS Step Functions, SQS, and EventBridge.
Contribute to frontend integrations to ensure AI-powered features are seamlessly delivered to end users.
Write clean, maintainable, and well-tested code across the technology stack.
Support cloud-native deployments, monitoring, troubleshooting, and production maintenance using AWS and Docker.
Ensure applications meet high standards of reliability, scalability, security, and operational performance.
Technical Leadership and Collaboration:
Lead architectural discussions and provide informed technical recommendations, clearly evaluating trade-offs between different engineering approaches.
Partner with Product, Design, and Delivery teams across global environments to define requirements, scope projects, and deliver end-to-end solutions.
Mentor engineers and help establish best practices for agentic AI development and AI-assisted engineering tools such as Claude Code.
Take full ownership of assigned features and releases, from initial design and implementation through debugging, optimization, deployment, and ongoing maintenance.
Contribute to engineering standards and technical strategies that support the long-term scalability and evolution of AI systems.
Promote a culture of technical excellence, accountability, collaboration, and continuous improvement.