AI Harness Engineer building reliable AI tools, agent harnesses, and P2P systems for Tether’s local AI platform. Developing QVAC features that connect users to scalable local AI.
About the role
- Staff AI Builder designing and shipping production GenAI systems for Robots and Pencils’ enterprise clients. Leading agentic AI, RAG, AWS architecture, reliability, and technical mentoring.
Responsibilities
- Lead the design and delivery of AI/ML systems
- Define AI architecture and lead model development and optimization
- Design and build agentic workflows, including reasoning loops, tool/function calling, and single- and multi-agent orchestration
- Build and maintain RAG pipelines with chunking, embeddings, OpenSearch vector search, re-ranking, and refresh handling
- Integrate AWS Bedrock and Agent Core, including MCP-based tool design
- Write and iterate on production system prompts
- Build evaluation and observability into agents using golden datasets, RAGAS-style metrics, LLM-as-judge, and tracing
- Design for LLM failures using retries, backoff, circuit breakers, fallback models, and user-facing degradation handling
- Build backend services in Python and Node.js, including AWS Lambda and API Gateway serverless architectures
- Design DynamoDB single-table schemas for conversation state, agent memory, and session history
- Support event-driven orchestration with Step Functions, SQS, and EventBridge
- Contribute to frontend integration points and write clean, tested code across the stack
- Support deployment, monitoring, and production troubleshooting in AWS and Docker environments
- Partner with product, design, and delivery leads across global teams to scope and ship features end-to-end
- Mentor engineers and improve agentic engineering practices, including use of Claude Code
- Own features and releases end-to-end, including debugging, hardening, and production reliability
Requirements
- 6+ years of professional software engineering experience, including meaningful time shipping GenAI/LLM-powered systems in production
- Hands-on depth in agentic AI, including reasoning loops, tool/function calling, and multi-agent orchestration
- Practical RAG expertise, including chunking strategies, embeddings, vector databases such as OpenSearch or similar, cosine similarity search, and re-ranking
- Experience building evaluation and observability for LLM systems, including golden datasets, LLM-as-judge, RAGAS or comparable metrics, and LangFuse/LangSmith or similar tracing tools
- Strong prompt engineering skills
- Hands-on experience with the AWS GenAI stack: Bedrock, Agent Core, Lambda, DynamoDB single-table design, S3, SQS, EventBridge, and Step Functions
- Strong Python and Node.js skills
- Experience building full-stack applications and RESTful APIs
- Understanding of production reliability patterns for LLM-backed systems, including retries/backoff, circuit breakers, and fallback models
- Experience with Docker and cloud-native deployment
- Ability to own ambiguous, integration-heavy problems and provide technically deep answers
- Helpful extras: direct Bedrock Agent Core experience; regulated or high-stakes domain experience; workflow orchestration familiarity; production LLM observability experience
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