The Opportunity

What You’ll Do

• Partner directly with clients to understand their objectives, workflows, technical environments, data, and operational constraints.
• Translate ambiguous business challenges into clear technical requirements, solution architectures, prototypes, and delivery plans.
• Design and build AI-enabled applications, agentic workflows, developer tools, APIs, data pipelines, and full-stack products.
• Engineer the harnesses around AI models, including prompts, tools, context, memory, permissions, runtime controls, observability, and feedback mechanisms.
• Build and refine agent loops that support planning, tool selection, execution, observation, reflection, recovery, and termination.
• Model complex workflows as graphs, state machines, or other orchestration patterns that coordinate models, tools, data, systems, and human approvals.
• Create evaluation frameworks that measure task completion, accuracy, reliability, safety, latency, cost, and user value.
• Develop rapid prototypes and proofs of concept, validate them with real users and representative evaluations, and evolve successful ideas into secure, scalable products.
• Integrate solutions with client systems, enterprise data sources, cloud platforms, and third-party services.
• Work across the technology stack, contributing wherever needed—from user interfaces and backend services to AI infrastructure, data workflows, and deployment pipelines.
• Implement appropriate safeguards, permission boundaries, approval steps, and fallbacks for autonomous and semi-autonomous systems.
• Instrument AI applications so teams can inspect traces, understand intermediate states, diagnose failures, and improve system performance.
• Identify and address failure modes such as hallucinations, looping, brittle tool calls, context loss, prompt injection, non-deterministic behavior, and unsafe actions.
• Troubleshoot complex technical and operational issues in client environments.
• Communicate technical decisions, risks, trade-offs, and progress clearly to both technical and non-technical audiences.
• Collaborate with product managers, designers, engineers, data specialists, security teams, and client stakeholders throughout the delivery lifecycle.
• Establish strong engineering practices, including automated testing, documentation, observability, security, and continuous delivery.
• Capture reusable patterns, components, and insights that strengthen TribalScale’s engineering capabilities and future client engagements.
• Support technical discovery, solution demonstrations, workshops, and pre-sales conversations when needed.

What You Bring

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

Nice to Have

• 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.

What Success Looks Like

• Turn loosely defined client needs into clear, executable technical plans.
• Deliver AI-enabled products that perform reliably outside of controlled demonstrations.
• Establish evaluation and observability practices that make quality measurable and regressions visible.
• Improve agent behavior by refining harnesses, loops, tools, graphs, context, and safeguards—not only by changing prompts or models.
• Help client teams understand what their AI systems can do, where they can fail, and how they should be operated responsibly.
• Create reusable technical patterns that accelerate future TribalScale engagements.
• Become a trusted partner who can navigate uncertainty, solve difficult technical problems, and help promising ideas reach production.

Why TribalScale

• Work on high-impact challenges across industries and technology domains.
• Build real products and production systems—not just recommendations or presentations.
• Work directly with clients and see the impact of your decisions.
• Explore emerging approaches to AI, agentic systems, product development, and software delivery.
• Collaborate with multidisciplinary teams that value experimentation and continuous learning.
• Influence both client outcomes and TribalScale’s evolving technical practices.
• Join an environment where ownership, diverse perspectives, and thoughtful innovation are encouraged.