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About This Role
About WalletGyde
WalletGyde is building a Financial AI platform designed to help people better understand, manage, and improve their finances through trustworthy and explainable AI.
As our product and internal AI infrastructure continue to mature, we are also exploring how agentic AI can improve the way our team operates internally. This includes AI project management, engineering coordination, workflow automation, human approval systems, and controlled multi agent workflows.
We are looking for an Agentic AI Systems Engineer Intern who is excited about building and debugging real AI agent infrastructure rather than only experimenting with prompts.
About the Role
You will help WalletGyde advance an internal agentic AI initiative focused on creating a secure, observable, and human controlled AI project management and engineering coordination system.
WalletGyde already has an existing multi agent foundation containing defined roles for technical coordination, software development, QA, and project management. Your responsibility will be to help reproduce, deploy, integrate, debug, and expand this architecture.
You will work closely with an existing automation team member who has already begun the project, while taking deeper ownership of the more technical implementation work involving runtime deployment, APIs, authentication, agent configuration, webhooks, service integration, and multi agent communication.
This is a strong opportunity for someone interested in the emerging field of Agentic AI Engineering and who wants hands on experience building systems where AI agents interact with real tools, workflows, and people.
What You Will Work OnAgent Runtime & Infrastructure
- Help deploy and maintain an OpenClaw based agent environment
- Configure and reproduce existing agent definitions, skills, identities, and workflows
- Debug runtime, dependency, configuration, and deployment issues
- Work with environment variables, secrets, logs, persistent state, and service health
- Help ensure agent environments are reproducible and properly documented
APIs & Agent Integration
- Build and test authenticated HTTP API connections between AI agents and external systems
- Define request and response payloads for agent workflows
- Work with REST APIs, JSON, webhooks, authentication, and service integration
- Help create stable interfaces that allow workflow systems to securely invoke AI agents
n8n & Workflow Automation
- Integrate deployed AI agents with n8n workflows
- Support HTTP Request nodes, webhooks, authentication, retries, and error handling
- Help create workflows where AI agents can analyze tasks, recommend actions, and return structured results
- Maintain human approval checkpoints before consequential actions
Multi Agent Systems
Help progressively validate workflows involving specialized agents such as:
Tech Lead Agent → Developer Agent → QA Agent → Technical Verification → Human Review
The initial goal is not unrestricted autonomy.
We want to build a controlled system where specialized AI agents can collaborate while humans retain authority over important decisions.
Tool & Platform Integration
Potential systems may include:
- GitHub
- Trello
- Discord
- n8n
- OpenClaw
- Replit or other cloud deployment environments
- LLM and model APIs
- Internal WalletGyde systems
Security & Observability
Work with our security team to help implement:
- Secure credential handling
- Agent authentication
- Permission boundaries
- Human approval controls
- Audit logs
- Agent activity tracing
- Failure handling
- Least privilege access
- Safe tool execution
What We Are Looking For
We are looking for someone who is technically curious, resourceful, and comfortable solving problems that may not yet have a documented answer.
Strong candidates may have experience with several of the following:
- Python and or TypeScript
- REST APIs
- JSON
- Webhooks
- Git and GitHub
- LLM APIs
- Tool calling
- Agent frameworks
- MCP concepts
- n8n, Zapier, Make, or similar automation platforms
- Docker or containerized applications
- Cloud deployment environments
- Environment variables and secrets
- Logging and debugging
- Databases
- Backend development
- Authentication and authorization
You do not need previous OpenClaw experience.
Experience with LangGraph, AutoGen, CrewAI, OpenAI Agents SDK, MCP, or other agent frameworks is also relevant.
The Type of Engineer We Want
The strongest candidate will not simply ask:
“What prompt should I use?”
We are looking for someone who asks:
“Why did the system fail, where did it fail, and how can we prove the fix works?”
You should enjoy:
- Investigating difficult technical problems
- Reading existing code and documentation before rebuilding something
- Working through deployment failures and logs
- Learning unfamiliar frameworks quickly
- Using AI coding tools while independently validating their output
- Documenting technical systems so another engineer can reproduce them
- Turning architecture diagrams into functioning systems
- Working in an early stage startup where technical requirements continue to evolve
Example Q4 Projects
During the internship, you may help WalletGyde:
- Reproduce an Existing AI Agent Architecture
Deploy the agent runtime and successfully reproduce at least one existing specialized agent.
- Validate Agent Runtime Health
Demonstrate that the agent can run reliably, authenticate correctly, expose logs, recover from failures, and respond to controlled requests.
- Connect AI Agents to n8n
Build an authenticated communication layer allowing n8n to send structured tasks to an AI agent and receive structured responses.
- Build Human Approval Workflows
Create systems where agents can recommend actions while important changes remain subject to explicit human approval.
- Demonstrate a Controlled Agent Workflow
Example:
Task → n8n → AI Agent → Recommendation → Human Approval → Approved Action → Audit Record
- Advance Multi Agent Collaboration
Once the underlying infrastructure is reliable, help test coordination between specialized Tech Lead, Developer, QA, and Project Management agents.
What Success Looks Like
By the end of the internship, we want to be able to demonstrate that WalletGyde can:
Deploy agents reliably
→ Call them securely through APIs
→ Connect them with workflow automation
→ Observe what they are doing
→ Maintain human control
→ Progressively expand toward useful multi agent workflows
Success will be measured by reliability, reproducibility, security, integration quality, and technical execution, not by the number of agents created.
Why This Internship Is Different
This is not a traditional machine learning internship focused primarily on training models.
It is also not a traditional DevOps internship.
You will work at the intersection of:
AI Agents
Backend Engineering
Automation
APIs
Cloud Infrastructure
Security
Workflow Orchestration
Human in the Loop Systems
Multi Agent Architecture
You will gain hands on exposure to one of the fastest evolving areas of applied AI while helping an early stage Financial AI company build its internal AI native operating infrastructure.
How to Apply
Please submit your resume along with a short response answering:
1. Tell us about an AI agent, automation, API integration, backend system, or technical project you have built. What did you personally implement?
2. Describe a difficult technical problem you encountered and how you debugged it.
3. If given an unfamiliar GitHub repository, a README, and a deployment environment, how would you approach getting the application running?
Links to GitHub projects, demos, technical portfolios, or other examples of your work are strongly encouraged.
WalletGyde, Inc. Building trustworthy Financial AI and exploring the next generation of AI native systems.
Job Type: Internship
Pay: From $100.00 per month
Work Location: Remote
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 9,653 AI roles we're tracking, AI Agent Developer positions make up 1% of the market. At Walletgyde, 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 $214,400 based on 180 positions with disclosed compensation. Entry-level AI roles across all categories have a median of $110,408.
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.
Walletgyde AI Hiring
Walletgyde has 2 open AI roles right now. They're hiring across AI/ML Engineer, AI Agent Developer. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $185,906 across 2,124 positions. About 6% of all AI roles offer remote work.
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
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