Overview

Professional experience building and deploying production-grade software, ideally including AI-powered or agentic systems.

Key Responsibilities

• Strong proficiency in Python, TypeScript, or another language commonly used to build AI applications, platforms, and developer tooling.
• A practical understanding of harness engineering: designing the infrastructure, context, tools, permissions, controls, and feedback mechanisms that enable AI agents to operate reliably.
• Experience building or working with agent loops, including planning, tool selection, execution, observation, reflection, recovery, and termination.
• Familiarity with graph-based orchestration, state machines, and multi-step workflows for coordinating agents, tools, data, and human approvals.
• Experience designing evaluation systems for AI applications, including task-based benchmarks, regression suites, model-graded evaluations, human review, and production monitoring.
• Understanding of the trade-offs involved in prompts, context management, memory, retrieval, tool use, structured outputs, and model selection.
• Experience instrumenting AI systems to trace decisions, inspect intermediate states, measure quality, and diagnose failures.
• Strong knowledge of APIs, distributed systems, databases, cloud infrastructure, and modern software-development practices.
• Ability to quickly understand unfamiliar systems, technologies, and business domains.
• Ability to move from an ambiguous customer problem to a working prototype, validate it with real users and evaluations, and evolve it into a secure, maintainable production system.
• Strong product judgment and the ability to balance model capability, reliability, latency, cost, security, and user experience.
• Experience collaborating directly with customers, stakeholders, or cross-functional delivery teams.
• Excellent written and verbal communication skills, including the ability to explain AI-system behavior, limitations, risks, and technical trade-offs clearly.
• Comfort working in fast-moving environments where requirements and technical constraints continue to evolve.
• A high degree of ownership, curiosity, empathy, and bias toward action.
• Ability to travel occasionally for client workshops or on-site delivery, where required.

We recognize that strong candidates may not meet every qualification listed. If this role excites you and your experience is adjacent to what we have described, we encourage you to apply.

Preferred Skills

• Experience with agent frameworks, workflow engines, coding agents, or custom model-orchestration systems.
• Experience creating tool interfaces, execution sandboxes, permission models, and human-in-the-loop controls for autonomous or semi-autonomous systems.
• Familiarity with retrieval-augmented generation, embeddings, vector search, knowledge graphs, fine-tuning, and model-routing strategies.
• Experience evaluating AI systems using golden datasets, simulators, adversarial testing, online experiments, and production feedback.
• Familiarity with AI observability, including traces, token and cost analysis, latency measurement, failure classification, and quality dashboards.
• Experience building systems that use multiple models or specialized agents to complete complex tasks.
• Understanding of AI security concerns, including prompt injection, data leakage, excessive agency, insecure tool use, and supply-chain risks.
• Experience with cloud platforms such as AWS, Microsoft Azure, or Google Cloud.
• Experience with containers, Kubernetes, infrastructure as code, and CI/CD tooling.
• Contributions to AI infrastructure, evaluation tooling, agent frameworks, or related open-source projects.
• Experience working in regulated or complex enterprise environments.
• Experience in consulting, professional services, solutions engineering, sales engineering, or another customer-facing technical role.
• A history of taking products from initial discovery through production launch and ongoing adoption.