Hire AI Agent Developers
Tap into elite AI Agent Developers from Latin America. Skilled in multi-agent systems, prompt design, and orchestration with fast team setup in 21 days.
Hire Remote AI Agent Developers
Daniel is an AI specialist crafting intelligent systems with practical user value.
- Model Training
- AI Architecture
- Data Engineering
- API Integration
- AI Product Development
Santiago excels in AI research with innovative insights and a knack for solving complex problems.
- Computer Vision
- Reinforcement Learning
- TensorFlow
- Deep Learning
- NLP
Esteban is an AI architect creating smart tools that solve complex, lasting challenges.
- AI Infrastructure
- Model Engineering
- System Integration
- Solution Design
- Data Pipelines
Angélica is an AI professional focused on designing ethical, intelligent systems.
- AI Research
- System Architecture
- Product Strategy
- Automation
- Data Analysis
Facundo is a dynamic AI researcher known for innovative solutions and insightful analysis.
- TensorFlow
- NLP
- Reinforcement Learning
- Computer Vision
- Deep Learning
"Over the course of 2024, we successfully hired 9 exceptional team members through Lupa, spanning mid-level to senior roles. The quality of talent has been outstanding, and we’ve been able to achieve payroll cost savings while bringing great professionals onto our team. We're very happy with the consultation and attention they've provided us."
“We needed to scale a new team quickly - with top talent. Lupa helped us build a great process, delivered great candidates quickly, and had impeccable service”
“With Lupa, we rebuilt our entire tech team in less than a month. We’re spending half as much on talent. Ten out of ten”
AI Agent Developers Skills
Autonomous Agent Frameworks
Build agents using LangChain, Auto-GPT, and similar frameworks.
Prompt Engineering
Design structured prompts for task-specific LLM performance.
Retrieval-Augmented Generation
Use vector search to enrich agents with external knowledge.
API Integration
Connect agents to tools and services through external APIs.
Memory Management
Add memory for contextual continuity and long-term relevance.
Agent Orchestration
Coordinate multiple agents to handle complex task flows.
AI Agent Developers Soft Skills
Problem Solving
Break down complex logic into manageable agent workflows.
Adaptability
Adjust quickly to evolving LLM tools and frameworks.
Communication
Explain AI agent behavior to technical and non-tech teams.
Collaboration
Work cross-functionally with PMs, designers, and devs.
Attention to Detail
Ensure precision in prompts, memory, and orchestration.
Curiosity
Explore and implement emerging AI techniques rapidly.
How to Hire AI Agent Developers with Lupa
Build autonomous systems faster with top AI Agent Developers. Use our Tech Recruiting Agency to access vetted LatAm talent, scale with Remote Staffing Services, or streamline hiring with our RPO solutions.
Together, we'll create a precise hiring plan, defining your ideal candidate profile, team needs, compensation and cultural fit.
Our tech-enabled search scans thousands of candidates across LatAm, both active and passive. We leverage advanced tools and regional expertise to build a comprehensive talent pool.
We carefully assess 30+ candidates with proven track records. Our rigorous evaluation ensures each professional brings relevant experience from industry-leading companies, aligned to your needs.
Receive a curated selection of 3-4 top candidates with comprehensive profiles. Each includes proven background, key achievements, and expectations—enabling informed hiring decisions.
Top candidates ready for your assessment. We handle interview logistics and feedback collection—ensuring smooth evaluation. Not fully convinced? We iterate until you find the perfect fit.
We manage contracting, onboarding, and payment to your team seamlessly. Our partnership extends beyond hiring—providing retention support and strategic guidance for the long-term growth of your LatAm team.
How to Write an Effective Job Post to Hire AI Agent Developers
Recommended Titles
- Autonomous AI Engineer
- AI Agent Framework Developer
- Conversational AI Engineer
- Multi-Agent Systems Developer
- AI Workflow Orchestration Specialist
- AI Software Engineer
Role Overview
- Tech Stack: Proficient in Python, LangChain, OpenAI APIs, and vector databases.
- Project Scope: Build autonomous AI agents for task orchestration and contextual reasoning.
- Team Size: Work alongside ML engineers and product managers in agile pods.
Role Requirements
- Years of Experience: Minimum of 3 years in AI/ML systems development.
- Core Skills: Autonomous agent architecture, tool integration, memory persistence.
- Must-Have Technologies: LangChain, OpenAI, Pinecone, FastAPI, Python.
Role Benefits
- Salary Range: $100,000 – $160,000 annually based on experience.
- Remote Options: Fully remote, with overlap to US business hours.
- Growth Opportunities: Work on cutting-edge agentic systems and multi-agent platforms.
Do
- List key skills in LLMs, prompt engineering, and tool orchestration
- Include remote work flexibility and async collaboration benefits
- Mention experience with agentic frameworks and APIs
- Highlight growth in AI-driven product automation
- Use precise, technically fluent job language
Don't
- Don’t generalize AI skills without detailing agent frameworks
- Don’t overlook orchestration or multi-tool environments
- Avoid vague job responsibilities lacking project scope
- Don’t exclude asynchronous and API-driven experience
- Refrain from generic language like “AI expert”
Top AI Agent Developer Interview Questions
Smart questions to assess AI Agent Developer expertise
How do AI agents differ from traditional rule-based systems?
Look for an explanation of autonomy, learning, and adaptability. A strong candidate will contrast reactive vs. deliberative agents and highlight agent decision-making frameworks.
What frameworks have you used for building agent-based systems?
The answer should include tools like LangChain, ReAct, or custom orchestration logic. Listen for context on coordination, memory management, and action selection.
Can you walk through an example of multi-agent collaboration?
Expect scenarios with communication between agents, task distribution, and conflict resolution. Look for applied use in automation, RPA, or AI pipelines.
How do you manage context and state in autonomous agents?
Candidates should explain persistent memory, vector databases, or caching layers. Look for a thoughtful approach to context window management and retrieval.
What challenges have you faced with AI agent orchestration?
Look for experience with latency, action planning, error handling, or tool integration. Bonus if they’ve implemented fallback or repair strategies.
Can you walk through how you debug an autonomous agent that isn’t responding as expected?
Look for systematic logging, observation of agent reasoning steps, and fallback strategies for failed action plans.
How do you decide between fine-tuning an LLM or adjusting prompt structures?
Expect analysis of performance trade-offs, cost implications, and iteration velocity needs.
Describe a time you had to refactor an orchestration flow. What was the challenge?
They should explain state complexity, task prioritization issues, or memory/context failures.
What would you do if an agent keeps looping through the same reasoning path?
Look for loop detection, memory resets, external feedback signals, or limiting reasoning steps.
How do you troubleshoot context loss in multi-agent workflows?
Expect structured memory architecture, vector retrieval verification, and temporal context reconstruction strategies.
Tell me about a time you had to balance multiple agent behaviors in a complex system.
Look for examples of orchestration, priority handling, and trade-offs between autonomy and control.
Describe how you handle ambiguity when designing AI agent interactions.
Expect reflection on experimentation, stakeholder input, and iterative design.
How have you collaborated with non-technical team members on AI functionality?
Look for communication clarity, ability to translate technical details, and shared understanding outcomes.
Describe a situation where your AI agent design didn’t meet expectations—what did you do?
Expect ownership of failure, diagnosis steps, and process improvement.
Have you ever faced ethical considerations when designing autonomous systems?
Look for responsibility, stakeholder consultation, and alignment with safety or fairness goals.
- Shallow understanding of agent frameworks
- Limited experience with orchestration logic
- Overreliance on prompt chaining without strategy
- Inability to articulate agent decision flows
- Resistance to collaboration in R&D settings
Build elite teams in record time, full setup in 21 days or less.
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