Forward Deployed Engineer
New
T
TribalScaleAI software
Remote USAFull-TimeMiddle
Salary not disclosed
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Job Details
- Required Skills
- PythonTypeScriptRESTful APIsDistributed Systems
Requirements
- Have professional experience building and deploying production-grade software.
- Be proficient in Python, TypeScript, or another language commonly used for AI applications, platforms, and developer tooling.
- Bring a practical understanding of harness engineering, including infrastructure, context, tools, permissions, controls, and feedback mechanisms.
- Have experience building or working with agent loops, including planning, tool selection, execution, observation, reflection, recovery, and termination.
- Be familiar with graph-based orchestration, state machines, and multi-step workflows.
- Have experience designing evaluation systems for AI applications, including benchmarks, regression suites, human review, or production monitoring.
- Understand trade-offs involving prompts, context management, memory, retrieval, tool use, structured outputs, and model selection.
- Have experience instrumenting AI systems to trace decisions, inspect intermediate states, measure quality, and diagnose failures.
- Have strong knowledge of APIs, distributed systems, databases, cloud infrastructure, and modern software-development practices.
- Be able to move from an ambiguous customer problem to a validated prototype and secure, maintainable production system.
- Have experience collaborating directly with customers, stakeholders, or cross-functional delivery teams.
- Be able to explain AI-system behavior, limitations, risks, and technical trade-offs clearly in writing and verbally.
- Be able to travel occasionally for client workshops or on-site delivery, where required.
Responsibilities
- Partner with clients to understand objectives, workflows, technical environments, data, and operational constraints.
- Translate ambiguous business challenges into 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 model harnesses, agent loops, and workflow orchestration patterns, including tools, context, permissions, controls, and human approvals.
- Create evaluation frameworks to measure task completion, accuracy, reliability, safety, latency, cost, and user value.
- Integrate solutions with client systems, enterprise data sources, cloud platforms, and third-party services.
- Instrument AI applications, diagnose failures, and address issues such as hallucinations, looping, context loss, prompt injection, and unsafe actions.
- Establish engineering practices including automated testing, documentation, observability, security, and continuous delivery.
- Communicate technical decisions, risks, trade-offs, and progress to technical and non-technical audiences.
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