Responsibilities

Every AI Agent Engineer at Orbis, regardless of focus area, owns the following:
Design, build, and deploy agentic AI workflows. Architect and ship the multi-agent and automated workflows that run in production: triage, data retrieval, summarization, reporting, and escalation routing.
Translate in both directions. Explain to non-technical stakeholders what the systems do, where they are reliable, and where they are not, in terms those stakeholders can act on. Equally, take a process owner's plain-language description of how their work happens and turn it into a workflow specification that holds up.
Partner with process owners to build what they actually need. Work directly and iteratively with the colleagues who own a process so the resulting capability fits their operation rather than forcing them to fit it. Push back constructively when a stated requirement will not survive contact with production.
Keep the workflows reliable. Monitor what is running in production, diagnose failures across prompts, tools, data, and orchestration, and resolve them. Know which problems are yours to solve and when to bring in engineering.
Own the cost of execution. Track what your AI workloads cost to run. Identify where a tighter prompt, a caching layer, or a simple deterministic step delivers the same outcome, and keep those costs defensible as volume grows.
Set the quality bar for AI-assisted output. Define accuracy standards, human review checkpoints, and escalation criteria for the automated workflows, and build the checks that prove those standards are being met.
Document what you build. Author and maintain the runbooks and SOPs that allow others to operate and troubleshoot these workflows without you. Nothing lives only in one person's head.
Integrate across Orbis products. Work across Catalyst, Pulse, and Discovery, with engineering support where needed, so workflows can access and act on the data they require.
Deliver the reporting leadership and the team depend on. Build and maintain the outputs behind resolution rate, escalation rate, output accuracy, response time, and employee satisfaction, and turn the Senior Operations Manager's requirements into structured, recurring reports and data exports.
Close capability gaps and keep the practice current. Recognize when a need is not served by existing tooling, scope the gap clearly, and either build it or bring it to engineering and product with enough detail to act on. Track developments in agentic AI tooling and enterprise automation platforms, and form a defensible view on what is worth adopting, at what pace, and for which use cases.
Focus Area — Client Experience (40% of the Role)

Qualification

Practical command of prompt engineeringSound judgment about model outputStrong written communicationExperience coding using PythonInterface buildSupport toolingClient accountabilityAI reliability practice

Required

Orbis weighs what a candidate has built over tenure or credentials. The following are true minimum requirements for this role:
Must have 5 years proven experience translating user requirements into wireframes, workflows and such.
Must have 1-year proven experience utilizing Claude or similar commercial offering to build AI agents.
Demonstrated experience building a functioning multi-agent workflow, with the ability to walk through the design rationale, how the agents hand off work and share context, what broke, and how it was fixed
Practical command of prompt engineering, including retrieval-augmented generation (RAG) and tool or function calling
Proven ability to explain technical work to non-technical audiences — describing a system's behavior, limits, and failure modes to a process owner or executive without hedging or retreating into jargon
Demonstrated ability to elicit requirements from stakeholders who cannot hand over a specification, and to reach a working solution regardless
Ability to troubleshoot agent workflows built on platforms such as Claude, GPT-class models, LangChain, AutoGen, or CrewAI, and to isolate whether a failure originates in the prompt, the tool, the data, or the orchestration
Working proficiency with APIs and sufficient scripting ability to build and debug workflows independently; software engineering experience is not required, but a JSON payload or a Python script must not be a blocker
Sound judgment about model output, including the discipline to state plainly when an output should not be trusted
Strong written communication. This role produces documentation, runbooks, and reports that program leads and executives rely on
Experience building and operating external-facing interfaces, portals, or client-facing applications in production

Preferred

Strong candidates will bring depth in one or more of the following. Do not screen yourself out for lacking items on this list.
5-7 years experience in software development
Experience coding using Python
Interface build: a modern front-end framework such as React; API design for external consumers; accessibility and usability practice
Support tooling: integration with ticketing or CRM platforms such as Zendesk, ServiceNow, or Jira Service Management
Client accountability: defining and reporting against service commitments; running client feedback loops or usability research
Bridging technical and operational teams: prior work as a solutions consultant, technical program manager, forward-deployed engineer, or in a product-adjacent role
AI reliability practice: monitoring, evaluation, or observability tooling for AI systems in production; automated evaluations or regression checks for LLM-based workflows; human-in-the-loop review steps that maintain quality without creating bottlenecks

AI Agent Engineer, Client Experience at Orbisoperations · LumiJobs

AI Agent Engineer, Client Experience

Responsibilities

Qualification

Required

Preferred

Benefits

Company

Orbisoperations

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