We're looking for a
Senior Agentic AI Engineer
to design, build, evaluate, and continuously improve sophisticated AI systems for a fast-moving enterprise AI platform.
You will work across multi-agent architectures, context graphs, memory systems, model routing, and automated evaluation frameworks to build production systems that support complex, real-world business decisions.
This is not an AI API integration or prompt-engineering role.
We are looking for someone who thinks deeply about how models, agents, tools, context, memory, and evaluation systems work together and can turn that understanding into reliable production systems.
The idóneo engineer combines strong software engineering fundamentals with deep curiosity, experimentation, and hands-on experience building modern agentic AI systems.
If that describes you, this role is a strong fit.
Why You'll Want to Join
You will be paid in
USD
(bi-monthly: every 15th and 30th)
Paid Time Off
in accordance with company policy
Observance of
Holidays
per company guidelines
100% remote setup
so you can work wherever you're most productive
This role requires availability during
US business hours
High ownership over AI architecture and technical direction at an early, high-leverage stage
Work directly with enterprise customers and see the real impact of what you build
What You'll Work On
Agentic AI Architecture
Design and build production-grade agentic and multi-agent systems
Architect specialized agents with clearly defined roles, tools, context, permissions, and decision boundaries
Design orchestration and routing strategies across agents, tools, models, and workflows
Build systems that intelligently use multiple LLMs and model providers depending on the task
Determine which models should power specific agents based on quality, reasoning capability, reliability, latency, and cost
Design failure handling, fallbacks, guardrails, and human-in-the-loop mechanisms
Agent and Context Graph Interactions
Design and manage how agents interact with context graphs, knowledge graphs, memory systems, and other context sources
Determine what information and which portions of a context graph individual agents should be able to access
Design context retrieval, filtering, ranking, and permission strategies based on each agent's role and task
Build and evaluate interactions between agents and nested or interconnected context graphs
Determine how much context an agent needs without unnecessarily increasing tokens, latency, or noise
Design systems that dynamically provide agents with the most relevant context for a specific task
Evaluate how changes to context availability affect agent accuracy, reasoning, reliability, and performance
AI Evaluation and Reliability
Design sophisticated evaluation frameworks for agentic AI systems




Build evals across model, agent, and context graph interactions
Systematically evaluate which models perform best for specific agents, tasks, and workflows
Evaluate which context, memory, or graph information should be available to different agents
Implement adversarial testing, model-to-model critique, LLM-as-judge, regression testing, and systematic evaluation
Identify failure modes and use evaluation results to continuously improve system architecture
Optimize AI systems across quality, accuracy, reliability, latency, token usage, and cost
Prefer measurable experimentation and evaluation over assumptions when making architectural decisions
Context, Memory, and Knowledge Systems
Design context and memory architectures for autonomous and semi-autonomous agents
Build with RAG, embeddings, retrieval systems, vector databases, memory systems, context graphs, and knowledge graphs
Design how information flows between context systems and individual agents
Determine how context should be retrieved, structured, filtered, and updated
Build context systems that support different agents, tasks, and levels of access
Balance context quality and completeness against token usage, latency, reliability, and cost
AI Harness Engineering
Design, customize, and extend AI harnesses and agent development environments
Work deeply with tools such as Claude Code, Claude Agent SDK, Codex, Cursor, MCP, agent frameworks, and similar technologies
Build custom instructions, skills, tools, context systems, feedback loops, and evals around AI models
Understand when existing AI tooling is sufficient and when customized harnesses or workflows are needed
Use AI extensively throughout the engineering lifecycle while maintaining strong technical understanding, ownership, and code quality
AI Research and Experimentation
Maintain an active, hands-on approach to staying current with rapidly evolving AI capabilities
Regularly explore new models, research, agent architectures, frameworks, and development tools
Turn promising research and emerging capabilities into experiments and prototypes
Evaluate whether new approaches can meaningfully improve existing production systems
Continuously evolve engineering practices as the AI ecosystem changes
Demonstrate curiosity and openness to challenging existing approaches rather than relying only on established patterns
Production Engineering
Turn ambiguous business problems into reliable production systems




Build and maintain backend services, APIs, databases, data pipelines, and asynchronous workflows
Contribute across the product stack when required including integrations and product-facing applications
Deploy and operate AI systems with appropriate testing, observability, logging, and monitoring
Design for enterprise requirements including security, permissions, data isolation, privacy, and reliability
What You Bring
5 or more years of professional software engineering experience including ownership of production systems
Deep, hands-on experience building production agentic AI and LLM systems beyond prototypes or simple API wrappers
Strong experience designing agents, multi-agent workflows, orchestration, and tool use
Production experience working across multiple LLMs and model providers with a strong understanding of model selection, routing, quality, reliability, latency, and cost tradeoffs
Advanced understanding of AI evaluation systems, experimentation, and failure analysis
Experience with adversarial evaluation, model-to-model critique, automated evals, or comparable approaches
Strong understanding of agent, model, and context graph interactions
Experience designing or managing how agents interact with context graphs, memory systems, knowledge graphs, or complex retrieval architectures
Deep understanding of context engineering, RAG, memory, retrieval, and context management
Experience with AI harness engineering or deeply customized AI development workflows
Hands-on experience with tools such as Claude Code, Codex, Cursor, Agent SDKs, MCP, or similar
Strong software engineering fundamentals with the ability to build reliable production systems independently
Strong systems thinking and ability to reason about second-order effects across interconnected AI systems
High ownership and comfort operating in ambiguous, fast-moving environments
Strong curiosity and willingness to continuously experiment with new AI approaches
Clear written and verbal English communication
Nice to Have
Hands-on experience with knowledge graphs, context graphs, Neo4j, or other graph databases
Experience building dynamic model-routing or agent-routing systems
Experience building automated AI evaluation infrastructure at scale
Experience with PostgreSQL, Redis, vector databases, and advanced search and retrieval
Experience with Python and or TypeScript
Frontend experience with React, Next.js, or similar frameworks
Experience with Docker, Kubernetes, cloud infrastructure, and infrastructure as code
Experience deploying AI systems inside enterprise VPCs or private environments
Experience with enterprise security, permissions, and sensitive data
Founding engineer or highly autonomous early-stage engineering experience
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📌 Full Stack Ai Engineer (Buenos Aires)
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