AI Engineer

Role Overview

We are seeking a forward-thinking AI Engineer with hands-on experience designing, building, and deploying intelligent AI agents capable of executing complex, multi-step tasks. This role focuses on developing modern agentic systems that combine large language models (LLMs), specialized agent skills, context management, and workflow orchestration to create reliable, scalable AI-powered solutions.

The ideal candidate understands how to move beyond traditional, step-by-step automation to build adaptive AI systems that can reason through tasks, select and use tools, maintain relevant context, and execute workflows with minimal human intervention. This position requires strong software engineering fundamentals, practical hands-on experience with AI agent architectures, and the ability to translate emerging AI capabilities into production-ready applications using technologies such as TypeScript, React, and n8n.

Key Responsibilities

• AI Agent Development: Design, build, and deploy intelligent AI agents capable of handling multi-step tasks, making context-aware decisions, using tools, and adapting their execution based on intermediate results.
• Agent Skills & Tool Integration: Develop reusable agent skills, tools, and capabilities that enable agents to perform specialized tasks, interact with external systems, and execute complex workflows efficiently.
• Context & Memory Management: Implement effective context engineering strategies, including context retrieval, memory management, conversation state, relevant information injection, and context-window optimization to improve agent performance and reliability.
• Agent Orchestration: Build systems that coordinate multiple tools, workflows, and agent capabilities while managing task decomposition, execution sequences, dependencies, error handling, and escalation when necessary.
• AI Workflow Automation: Use n8n and other appropriate technologies to design, connect, and optimize automation workflows that integrate AI models, APIs, business logic, and external applications.
• Full-Stack Development: Use TypeScript and React to develop and integrate AI-powered features into software applications, building reliable interfaces and backend integrations that support agent execution.
• LLM & API Integration: Integrate leading large language models and third-party APIs, selecting appropriate models and implementation approaches based on task complexity, performance, reliability, and cost.
• Evaluation & Optimization: Test and evaluate agent behavior using representative tasks, structured test cases, and measurable performance criteria. Identify failure patterns and continuously improve accuracy, consistency, latency, and execution quality.
• Production Deployment & Monitoring: Deploy AI solutions into production environments, monitor their behavior, troubleshoot failures, and implement safeguards to maintain dependable operation.
• Version Control & Collaboration: Use GitHub and established software development practices to maintain code quality, manage changes, document implementations, and collaborate effectively with the engineering team.
• Continuous Improvement: Stay current with evolving agent frameworks, model capabilities, tool-calling patterns, and context engineering techniques, applying relevant advancements to improve existing solutions.

Requirements & Qualifications

• Professional Experience: 3–5+ years of professional software engineering experience, including hands-on development of AI-powered applications or intelligent automation systems.
• AI Agent Engineering: Demonstrated experience building and deploying AI agents beyond basic chatbot implementations or purely linear, rule-based automation workflows, and ability to articulate and walk clients and users through the ai agent development process.
• Agent Skills & Tool Use: Practical understanding of how to define, implement, and reuse agent skills, tool integrations, and specialized capabilities to support complex task execution.
• Context Engineering: Experience managing context, memory, state, and information retrieval to help agents maintain relevant information and perform reliably across multi-step tasks.
• LLM Integration: Experience working with large language models, API integrations, structured outputs, tool calling, and prompt design in real-world applications.
• Programming Skills: Strong proficiency in TypeScript and solid experience with React. Ability to write maintainable code, debug complex issues, and integrate AI capabilities into production software.
• Workflow Automation: Hands-on experience with n8n or comparable workflow automation and orchestration platforms.
• Agent Architecture: Understanding of modern agentic design patterns, including task planning, tool selection, execution loops, state management, error recovery, and human-in-the-loop controls where appropriate.
• Software Engineering Practices: Familiarity with Git, GitHub, API design, testing, debugging, and collaborative development workflows.
• Production Readiness: Ability to take an AI solution from concept and prototyping through implementation, testing, deployment, and ongoing optimization.
• Problem-Solving: Strong analytical skills and the ability to evaluate different technical approaches, make sound implementation decisions, and balance development speed with maintainability and reliability.
• Ownership & Autonomy: Self-directed and comfortable working in a remote environment, managing priorities, and delivering technical projects with limited supervision.

Preferred Qualifications

• Experience with AI agent frameworks, SDKs, or orchestration libraries such as LangGraph, LangChain, or comparable technologies.
• Familiarity with the Model Context Protocol (MCP) and integrating agents with external tools and services.
• Experience with Python or other programming languages commonly used in AI and backend development.
• Knowledge of retrieval-augmented generation (RAG), embeddings, vector databases, semantic search, and knowledge retrieval.
• Experience implementing agent evaluation frameworks, observability, tracing, guardrails, and automated testing for AI systems.
• Familiarity with cloud platforms such as AWS, Google Cloud, or Microsoft Azure.
• Understanding of MLOps, CI/CD, application security, and scalable deployment practices for AI-powered applications.

Contract Perks

• Performance-based scheduled rate increases.
• Minimum 5 paid days off annually, plus holidays, subject to applicable contract terms.
• Annual cost-of-living adjustments at the one-year anniversary, subject to contract terms.