Applied AI Engineer

PortugalRemoteFull-timemid

Machine Learning EngineerData

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Quick Summary

Key Responsibilities

Design and build essential components of agentic systems, including memory, context management, tool-calling capabilities, orchestration mechanisms, and feedback loops.

Requirements Summary

Strong software engineering background, with proven experience independently delivering complex systems from initial concept through production deployment.

Technical Tools

Machine Learning EngineerData

As an Applied AI Engineer, you will play a pivotal role in designing and building the next generation of intelligent, agentic systems.
You will develop the core infrastructure that enables AI agents to manage context, use tools, coordinate tasks, and continuously improve their performance.
Working at the intersection of advanced AI research and practical software engineering, you will transform complex operational workflows into reliable, scalable production solutions.
You will establish robust evaluation, testing, and observability frameworks to ensure AI-powered systems deliver predictable and measurable results.
You will also explore innovative ways to integrate coding agents and specialized subagents into software development processes.
Collaborating closely with product and operational teams, you will identify high-impact automation opportunities and translate them into effective technical solutions.
As an early core member of the engineering team, you will have significant influence over architectural decisions, technical standards, and the long-term AI strategy.

In this role, you will design and implement the technical foundations of agentic systems while driving the adoption of AI-powered engineering workflows. Your responsibilities will include:

Design and build essential components of agentic systems, including memory, context management, tool-calling capabilities, orchestration mechanisms, and feedback loops.
Develop reusable AI infrastructure that enables new workflows to be introduced quickly, consistently, and with predictable reliability.
Build comprehensive evaluation frameworks to measure agent performance, identify failure points, and understand the factors behind successful outcomes.
Integrate testing, tracing, debugging, and evaluation into the software engineering lifecycle as fundamental practices.
Develop systems and validation processes that allow agentic workflows to be thoroughly assessed before production deployment.
Advance the use of large language models (LLMs) in software development by creating sophisticated AI-assisted engineering workflows.
Build workflows where coding agents and specialized subagents can explore code repositories, implement changes, review architecture, validate conventions, run evaluations, and iterate with minimal human intervention.
Actively use agentic engineering tools and workflows in daily development activities.
Design architectures that enable specialized AI agents, tools, deterministic software, and human expertise to work together effectively.
Determine when to use LLMs, traditional software solutions, human intervention, or coordinated multi-agent approaches to achieve the best outcomes.
Collaborate with operational and product teams to understand existing processes, identify high-value automation opportunities, and transform them into reliable production systems.
Establish scalable architectural patterns and engineering standards for agentic systems as the organization grows.
Take ownership of technical decisions and contribute to the overall direction of the AI engineering function.

Requirements

~2 min read

The ideal candidate is a strong software engineer with hands-on experience building production-ready AI systems and the autonomy to solve complex technical challenges in a rapidly evolving environment. The following qualifications and skills are expected:

Strong software engineering background, with proven experience independently delivering complex systems from initial concept through production deployment.
Practical experience building and deploying AI or agentic systems that have operated in production environments.
Demonstrated understanding of real-world AI engineering challenges, including reliability, latency, context management, failure modes, evaluation, and observability.
Strong knowledge of agent architectures, tool use, structured outputs, context management, orchestration, evaluation frameworks, and feedback loops.
Advanced practical experience with modern LLMs and the ability to build reliable workflows around probabilistic AI systems.
Hands-on experience using coding agents and agentic development workflows as part of everyday software engineering.
Ability to break down complex engineering tasks across multiple agents or specialized subagents and implement mechanisms for validating and improving their outputs.
Strong architectural judgment and the ability to select the right combination of AI models, deterministic software, and human intervention for different challenges.
Experience with TypeScript and Node.js, or strong experience developing AI systems in Python combined with the willingness and ability to work in a TypeScript-first environment.
High level of autonomy, ownership, and confidence making technical decisions in ambiguous situations.
Strong product mindset and the ability to connect engineering decisions with operational efficiency and business outcomes.
Excellent communication and collaboration skills, with fluency in English.
Startup mentality, including resilience, adaptability, initiative, and the ability to thrive in a fast-paced environment.

What We Offer

~2 min read

✓Competitive Compensation: Annual salary ranging from €100,000 to €150,000.

✓Remote Work: Opportunity to work remotely within Europe or the United Kingdom.

✓Early-Team Influence: Join as an early core engineering team member with significant ownership over technical direction and architectural decisions.

✓Technical Autonomy: Shape engineering standards, development practices, tooling, and the foundations of the organization's agentic AI infrastructure.

✓Cutting-Edge Technology: Work extensively with advanced LLMs, autonomous agents, multi-agent architectures, and AI-powered software development workflows.

✓Meaningful Impact: Build production systems that address real operational challenges and deliver measurable business value.

✓Cross-functional Collaboration: Work closely with product and operational teams to identify opportunities and develop practical automation solutions.

✓Professional Growth: Take on challenging engineering problems while helping establish the foundations of a growing AI-focused organization.