Staff Engineer / AI Builder

Accountabilities: • 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.

Requirements:

• At least 6 years of professional software engineering experience, including substantial hands-on experience developing and deploying generative AI or LLM-powered systems in production environments.

• Strong practical expertise in agentic AI engineering, including reasoning loops, tool and function calling, and single-agent and multi-agent orchestration architectures.

• Ability to evaluate architectural approaches and explain when single-agent systems are more appropriate than multi-agent solutions.

• In-depth knowledge of RAG architectures, including document chunking strategies, embeddings, vector databases, OpenSearch or equivalent technologies, cosine similarity search, and re-ranking techniques.

• Experience building evaluation and observability frameworks for LLM applications, including golden datasets, LLM-as-judge methodologies, RAGAS or comparable evaluation metrics, and tracing platforms such as LangFuse or LangSmith.

• Advanced prompt engineering capabilities, with experience designing structured, production-ready system prompts that include clear instructions, constraints, and output requirements.

• Hands-on experience with the AWS generative AI ecosystem, particularly Bedrock, Agent Core, Lambda, DynamoDB, S3, SQS, EventBridge, and Step Functions.

• Strong programming skills in Python and Node.js, with experience developing backend services, full-stack applications, and RESTful APIs.

• Solid understanding of production reliability and resilience patterns for AI-powered systems, including retry mechanisms, exponential backoff, circuit breakers, and fallback model strategies.

• Experience with Docker, containerization, and cloud-native application deployment.

• Ability to independently manage complex, ambiguous, and integration-heavy engineering challenges, taking ownership from initial problem definition through production delivery.

• Strong analytical and problem-solving skills, combined with the ability to communicate complex technical decisions and justify architectural recommendations.

• Demonstrated technical leadership, mentoring capabilities, and the ability to collaborate effectively with multidisciplinary and distributed teams.

Nice to Have:

• Direct experience with Amazon Bedrock Agent Core, including agent registration, MCP tool integration, memory and session management, and gateway provisioning.

• Previous experience working in regulated or high-stakes industries such as healthcare, financial services, or education, where system reliability, security, and evaluation rigor are essential.

• Familiarity with workflow orchestration platforms such as AWS Step Functions, Sequencer, or similar state-machine-based technologies.

• Practical experience implementing and operating LLM observability tools in production environments.

• Experience delivering enterprise-grade AI solutions that integrate with complex business systems and operational workflows.

Benefits:

• Competitive Compensation: Annual salary ranging from $165,604 to $228,491 USD , depending on qualifications, experience, skills, seniority, geographic location, and other relevant factors.

• Comprehensive Healthcare: Medical, dental, and vision insurance for eligible employees.

• Retirement Savings: Access to a 401(k) retirement savings plan for eligible employees.

• Paid Time Off: Paid leave to support personal needs, rest, and work-life balance.

• Professional Development: Opportunities to work on advanced AI technologies and solve challenging enterprise engineering problems.

• Technical Ownership: Significant autonomy and responsibility in shaping AI architecture, engineering standards, and production systems.

• Collaborative Environment: Work alongside multidisciplinary teams in an innovation-driven environment focused on creativity, technical excellence, and measurable client impact.

• Career Growth: Opportunities to strengthen technical leadership skills, mentor other engineers, and influence the evolution of enterprise AI solutions.

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