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About This Role
Gravitee is a 2025 Gartner Magic Quadrant Leader, on a mission to govern the world’s intelligence.
We deliver the industry’s most advanced platform for Any API, Any Event, and Any AI Agent, trusted by global leaders like Michelin, Roche, and Blue Yonder.
Why join us?
- The Mission: We are the first to bridge traditional API Management with the new frontier of AI Agent Security
- The Momentum: A high\-growth Leader \- combining market credibility with startup speed
- The DNA: We hire people who *Hold Nothing Back* \- passionate builders who want to redefine digital infrastructure
*Don’t just watch the AI revolution. Build the infrastructure that controls and secures it.*
### The Role
We're looking for a Product Engineer to help build the core of how enterprises secure, govern, and control AI agents – spanning the Agent Gateway that governs agent\-to\-agent (A2A) communication, and the LLM control layer that gives enterprises centralized command over every LLM their agents call. You'll work close to customers and close to the code, owning a slice of the Agent Gateway or LLM control roadmap end to end – depending on where your strengths and interests point. This is a role for someone who wants to own outcomes, not just tickets: you'll help decide what gets built, then build it.
### What You’ll Be Doing
At Gravitee, impact isn’t abstract, it’s visible. In this role, you will:
- Own a meaningful slice of the Agents or LLM control roadmap, from customer conversations through shipping and iterating on the result.
- Build features for the Agent Gateway and A2A protocol support, or for multi\-LLM routing, cost control, and reliability – depending on your pod – working closely with Product Management and Design.
- Use AI\-assisted development tools as a core part of your workflow to move faster from idea to shipped feature.
- Dig into usage data and customer feedback to help decide what to build next, not just how to build it.
- Prototype quickly, test with real users, and be willing to change course when something isn't landing.
- Partner with Staff Engineers and other pods where the Agents surface touches LLM control, MCP, or the core API gateway – and vice versa.
*Your impact will be visible, measurable, and felt by every enterprise trusting agents with real access to their systems, and every customer routing traffic through Gravitee's LLM control layer.*
### Essential Skills
- 4\+ years of software engineering experience, ideally in a product\-facing or full\-stack role.
- Comfortable talking directly to customers and translating what you hear into a technical plan.
- Hands\-on experience with AI coding assistants (Cursor, Claude Code, GitHub Copilot or similar) as part of daily work.
- Strong fundamentals in API design and distributed systems.
- A bias toward shipping and iterating over building the theoretically perfect solution.
### Desired Skills
- Familiarity with the A2A protocol or agent\-to\-agent communication patterns.
- Experience with API gateways or API management platforms.
- Exposure to LLM\-based products or agentic workflows.
- Experience integrating multiple LLM providers into a single product surface.
- Experience with cost or usage governance for AI and LLM systems.
- Familiarity with the Model Context Protocol (MCP) or similar orchestration standards.
- Experience working in a fast\-moving, ambiguous product environment.
### Who Thrives at Gravitee
At Gravitee, our growth is powered by people who bring passion to what they build, act with professionalism in how they work, and hold nothing back in their commitment to doing things well.
You’ll do well here if you:
- Care deeply about quality, clarity, and impact
- Are curious, adaptable, and excited by emerging technologies like AI
- Take ownership and follow through
- Value collaboration, openness, and continuous improvement
*Bonus points if you’ve worked with APIs, cloud\-native platforms, AI\-enabled systems, or open source, but curiosity matters most.*
### Life at Gravitee
At Gravitee, we invest in humans, not just roles.
You’ll get:
- Competitive medical coverage
- Pension / 401k program options
- Stock options \- you build it, you own it
- 25 days holiday \+ in\-country national holidays
- 3 mental health days \+ wellness allowance
- Your birthday off
- Professional development budget to fuel your growth
- Hybrid work culture with hubs across regions
- Quarterly team events \+ annual offsite at an exciting location
- A meaningful, progressive, global company culture that is as fun as it is hardworking
- Endless growth opportunities
Salary: Up to $188,000 (Base \+ Variable)
*At Gravitee, we believe diverse perspectives make better products and stronger teams.*
*At Gravitee, no employee or applicant will be treated less favorably on the grounds of sex, marital status, race, color, nationality or ethnic or national origin, disability, gender, sexual orientation, gender identity, age, pregnancy or maternity, marital or civil partner status, or religion or belief.*
*By clicking submit below, you consent to allow Gravitee to store and process the personal information submitted above.*
Role Details
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 4,317 AI roles we're tracking, AI Agent Developer positions make up 1% of the market. At gravitee.io, 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
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 $240,000 based on 96 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
gravitee.io AI Hiring
gravitee.io has 1 open AI role right now. They're hiring across AI Agent Developer. Based in Denver, CO, US.
Location Context
AI roles in Denver pay a median of $199,950 across 66 tracked positions. That's 7% below the national 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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
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