AI Agent Engineer, Client Experience

McLean, VA, US Mid Level AI Agent Developer

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Skills & Technologies

AutogenCatalystClaudeCrewaiLangchainPrompt EngineeringPythonRag

About This Role

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Company Overview

Orbis is a technology company that delivers sovereign intelligence and decision capabilities to the United States Government and its allies. We convert complex information and operational systems into decisive outcomes, giving public institutions and critical enterprises the tools to move faster, see clearer, and act first in an increasingly contested world.

We are not a consulting firm and not a systems integrator. Every Orbis team is built around practitioners who have operated at the mission edge, and every engagement is judged on one thing: whether it produces working capability, not slide decks. Solutions forged from experience; that is how we build, and it is how we hire.

Job Summary

Orbis is building the agentic AI capability its mission support operations run on, and the AI Agent Engineer builds and operates it. Sixty percent of this role is common to every AI Agent Engineer at Orbis: designing, building, and deploying the multi-agent workflows behind triage, data retrieval, summarization, reporting, and escalation routing. The remaining forty percent is this role's focus — owning how clients get support on Orbis products already in the field. This engineer owns the Mission Support Desk (MSD) workflows behind client requests — intake, triage, escalation, and resolution — operates the client-facing surfaces those workflows run on, and holds the response and resolution commitments Orbis makes on deployed products.

Travel: Occasional (up to 10%), which may include international travel.

Key Responsibilities — Common Core (60% of the Role)

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)

In addition to the common core above, this role owns what the client sees and touches:

  • Build and operate the external-facing interfaces and portals through which clients interact with Orbis capability.
  • Design and automate client onboarding: provisioning, configuration, and the first-run experience.
  • Build the support workflows behind client requests — intake, triage, escalation, and resolution — and instrument them so performance is measurable.
  • Hold the reliability and accuracy bar on anything client-facing, including documented failure modes and response commitments.
  • Turn client friction into product change: capture where clients struggle and carry it back to engineering and product with enough detail to act on.
  • Maintain the client-facing documentation, release notes, and status communication that keep clients informed without a support request.

Required Qualifications

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
  • Experience designing or automating customer onboarding and provisioning
  • Experience designing support workflows — intake, triage, escalation, resolution — and instrumenting them so performance is measurable

Desired Qualifications

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
  • Process design: designing SOPs or escalation frameworks that hold up across multiple stakeholders
  • AI-assisted development tooling: working fluency with Claude, Copilot, or Cursor
  • Mission environment: operating within or supporting a government or defense program; OSINT, DIGINT, or intelligence analysis workflows and tradecraft; data governance concepts, federated access control models, or zero-trust architectures
  • Operating range: a track record of coming up to speed quickly on unfamiliar systems, and effectiveness in a distributed, multi-program environment with competing priorities

Physical Requirements

  • Prolonged periods of sitting at a desk and working on a computer
  • Participation in virtual and in-person meetings
  • Ability to attend planned meetings and/or work in classified spaces for extended periods within the specified work regions

Travel: Occasional (up to 10%), which may include international travel.

Orbis Benefits

Beyond the opportunity to work alongside practitioners who have operated at the mission edge, joining Orbis means real ownership over outcomes that matter; the chance to build technology that gives the United States Government and its allies a genuine decision advantage, not just another dashboard. From competitive retirement matching to a PTO policy that encourages real time off, we're committed to creating an environment where our team can do their best work and still have a life outside of it.

Our comprehensive benefits package is designed to meet the diverse needs of our employees and their families. A full list of benefits is shared with candidates following an initial conversation with our HR team, so you'll have a clear picture of the support and resources available to you as part of the Orbis team.

Orbis Locations

Orbis works in whatever arrangement the mission requires. Depending on the role, that might mean five days a week on a customer site, full-time in one of our offices, a hybrid schedule, or fully remote work; our team is spread across 25 states and four countries to match. We make sure every team member has the tools and access needed to do the work, wherever that work happens.

Travel is common across many Orbis roles, particularly those supporting customer missions in the field.

Orbis is headquartered in McLean, VA, with additional office locations in Taipei, Taiwan, and Canberra, Australia.

Orbis is an Equal Employment Opportunity employer. We are committed to providing a work environment free of discrimination and harassment, and we make employment decisions based on merit,

qualifications, and business need. Orbis does not discriminate on the basis of race, color, religion, sex, sexual orientation, gender identity or expression, national origin, age, disability, protected veteran status, genetic information, or any other status protected by applicable law.

Orbis complies with all applicable equal opportunity and non-discrimination laws in every jurisdiction in which we operate. Reasonable accommodation is available to applicants and employees with disabilities upon request.

Role Details

Company ORBIS SE
Title AI Agent Engineer, Client Experience
Location McLean, VA, US
Experience Mid Level
Salary Not disclosed
Remote No

About This Role

AI Agent Developers build autonomous systems that can reason, plan, and take actions. They design multi-step workflows, tool-use frameworks, and orchestration layers that let LLMs interact with external systems. This is the frontier of applied AI engineering.

Agent development is where the most interesting (and hardest) problems in applied AI live right now. Making an LLM answer a question is straightforward. Making it reliably execute a 15-step workflow that involves calling APIs, reading databases, making decisions, and recovering from errors is an unsolved problem. You're building systems that have to work despite the fact that the underlying model is non-deterministic.

Across the 9,653 AI roles we're tracking, AI Agent Developer positions make up 1% of the market. At ORBIS SE, this role fits into their broader AI and engineering organization.

AI Agent Developer is one of the newest and fastest-growing AI role categories. The market is early but accelerating as companies move beyond simple chatbots toward AI systems that can take real actions. Compensation is high because the skill set is rare and the business impact is potentially enormous.

What the Work Looks Like

A typical week includes: designing the action space and tool definitions for a new agent use case, debugging why the agent chose the wrong action sequence on a specific input, building evaluation frameworks that test agent reliability across hundreds of scenarios, optimizing the prompt chain for cost and latency, and implementing safety guardrails to prevent the agent from taking destructive actions. The work is equal parts engineering and empirical science.

AI Agent Developer is one of the newest and fastest-growing AI role categories. The market is early but accelerating as companies move beyond simple chatbots toward AI systems that can take real actions. Compensation is high because the skill set is rare and the business impact is potentially enormous.

Skills Required

Autogen (1% of roles) Catalyst Claude (4% of roles) Crewai (1% of roles) Langchain (3% of roles) Prompt Engineering (4% of roles) Python (17% of roles) Rag (7% of roles)

Deep experience with LLM APIs and agent frameworks (LangChain, CrewAI, AutoGen). Strong understanding of prompt engineering, function calling, and error handling for non-deterministic systems. Python is standard. Experience with orchestration patterns, state management, and workflow engines adds significant value.

The best agent developers think like systems engineers. They design for failure modes, build observability into every step, and understand that agent reliability is the product. Expertise in evaluation methodology for non-deterministic systems is the differentiator. Can you measure whether your agent works 'well enough'? Can you find the edge cases where it breaks?

Look for roles that describe specific agent use cases, mention evaluation methodology, and talk about production deployment. Early-stage companies exploring agents can be exciting, but be prepared for ambiguity. The most valuable roles are at companies that have already shipped a v1 and need to make it reliable.

Compensation Benchmarks

AI Agent Developer roles pay a median of $214,400 based on 180 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.

Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.

ORBIS SE AI Hiring

ORBIS SE has 3 open AI roles right now. They're hiring across AI Agent Developer. Based in McLean, VA, US.

Location Context

Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 roles, $210,000 median).

Career Path

Common paths into AI Agent Developer roles include Software Engineer, LLM Engineer, Prompt Engineer.

From here, career progression typically leads toward AI Architect, Principal Engineer, Head of AI Engineering.

Build agents. That's the portfolio. Take an open-source agent framework, build something that completes a non-trivial multi-step task, evaluate it rigorously, and document what you learned about reliability, cost, and failure modes. The field is new enough that practical experience counts for more than credentials.

What to Expect in Interviews

Interviews focus on systems thinking and reliability engineering. Expect questions about agent architecture: how you'd design a multi-step workflow with error recovery, how you'd evaluate agent performance, and how you'd prevent agents from taking destructive actions. Coding exercises often involve building a simple agent with tool use and evaluating its behavior across different scenarios. Discussion of safety and guardrails is increasingly common.

When evaluating opportunities: Look for roles that describe specific agent use cases, mention evaluation methodology, and talk about production deployment. Early-stage companies exploring agents can be exciting, but be prepared for ambiguity. The most valuable roles are at companies that have already shipped a v1 and need to make it reliable.

AI Hiring Overview

The AI job market has 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.

The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 roles).

AI Agent Developer is one of the newest and fastest-growing AI role categories. The market is early but accelerating as companies move beyond simple chatbots toward AI systems that can take real actions. Compensation is high because the skill set is rare and the business impact is potentially enormous.

The AI Job Market Today

The AI job market spans 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.

The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (193) are outnumbered by mid-level (4,500) and senior (3,455) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,505 positions, representing the bottleneck between technical execution and organizational strategy.

Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.

AI compensation is structured in clear tiers. The market median sits at $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.

Category matters for compensation. AI Safety roles lead at $253,100 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.

The most in-demand skills across all AI postings: Python (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.

Frequently Asked Questions

Based on 180 roles with disclosed compensation, the median salary for AI Agent Developer positions is $214,400. Actual compensation varies by seniority, location, and company stage.
Deep experience with LLM APIs and agent frameworks (LangChain, CrewAI, AutoGen). Strong understanding of prompt engineering, function calling, and error handling for non-deterministic systems. Python is standard. Experience with orchestration patterns, state management, and workflow engines adds significant value.
About 6% of the 9,653 AI roles we track offer remote work. Remote availability varies by company and seniority level, with senior and leadership roles more likely to offer location flexibility.
ORBIS SE is among the companies actively hiring for AI and ML talent. Check our company profiles for detailed breakdowns of open roles, salary ranges, and hiring trends.
Common next steps from AI Agent Developer positions include AI Architect, Principal Engineer, Head of AI Engineering. Progression depends on whether you lean toward technical depth, people management, or product strategy.

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