AI Engineer

Critical Manufacturing is dedicated to empowering high-performance operations to make Industry 4.0 a reality with the most innovative, comprehensive, and modular MES software. We have a global presence, but our headquarters, and the main technical center, are in Porto (Maia), Portugal, where we develop a state-of-the-art solution for Semiconductor, Electronics, Medical Devices, and other Discrete industries.Recognized for the third consecutive year as a Leader by Gartner, we are part of ASMPT, the world's largest supplier of best-in-class equipment, and technological process partner for the electronics and semiconductor industries.The role:You will join an existing AI engineering team focused on building reliable AI infrastructure for manufacturing systems. This is hands-on work developing MCP servers, creating tooling for model observability, telemetry, and retraining pipelines—no leadership required, just solid execution within a collaborative team.This role is based at our headquarters in Porto, Portugal, where collaboration, experimentation, and rigorous engineering standards are essential. You’re expected to stay closely connected—actively participating in technical design reviews, architecture discussions, and engaging with teams across Product, Data, and Platform Engineering. This is a role for someone who cares about building AI systems that are not just smart, but observable, debuggable, and continuously improving.What you’ll do:Develop MCP Servers Implement and maintain Model Context Protocol (MCP) servers that connect language models to manufacturing domain tools and data sourcesOptimize server performance and define clear interfaces for tool integration, ensuring models have safe, reliable access to business logicCollaborate with team leads to map complex manufacturing workflows into structured tools and promptsBuild Model Observability and Telemetry InfrastructureDesign and implement comprehensive telemetry systems to track model behavior, token usage, latency, and cost in productionCreate dashboards and alerting systems that give real-time visibility into model performance and anomaliesInstrument models to capture structured traces: prompts/system context, tool invocations, inputs/outputs, intermediate artifacts, and decision metadataContribute to standards for logging, tracing, and distributed observability across all AI systemsDevelop Retraining and Continuous Improvement PipelinesBuild data collection pipelines that capture production interactions, model failures, and edge cases for retrainingImplement automated systems for evaluating model improvements and managing safe rolloutsContribute to feedback loops that allow the platform to learn from real-world usage without manual interventionSupport Team DeliverablesWrite clean, testable code and contribute to team codebases, documentation, and CI/CD processesParticipate in code reviews, technical design reviews, and troubleshooting production issuesExperiment with new tools and techniques under team guidance to improve AI system reliabilityPromote the adoption of agentic coding across teams to accelerate delivery and increase throughput while maintaining quality and security standardsDesign repositories, CI, and developer tooling that make agent-driven changes safe (linting, typed APIs, contract tests, golden tests, eval gates)Ensure Production ReliabilityImplement robust error handling, fallback strategies, and graceful degradation for AI systemsMonitor and tune AI systems for performance, uptime, and safety in manufacturing environmentsGather feedback from operations and product teams to refine tooling and server implementationsWhat Success Looks LikeWithin your first year, you will have: Deployed production MCP servers handling real manufacturing workloadsBuilt and iterated on observability tools used daily by engineering and ops teamsContributed to retraining pipelines that reduce model staleness and improve prediction accuracyEstablished clear patterns and best practices that help the team scale AI systems reliablyDelivered robust tooling for debugging, monitoring, and managing AI systems in manufacturing environmentsWhy Join Us Work on AI that powers real factories, solving problems with immediate industrial impactJoin a tight-knit engineering team building the backbone of trustworthy AI infrastructure for manufacturingContribute to systems that manufacturers depend on daily, with full observability and reliabilityEnjoy the freedom to code, collaborate, and grow technically in a rigorous engineering environment What You Will Bring At least 1 year of hands-on machine learning experience, including training and testing models, and a practical understanding of overfitting, generalization, and bias; plus a solid grasp of common model families (e.g., k-nearest neighbors, decision trees/random forests, support vector machines, linear/logistic regression, and basic neural networks)At least 1 year of hands-on experience with LLMs in production or applied settings, including inference, prompt engineering, and evaluation; with a working understanding of how LLMs are configured and behave (e.g., temperature, top-p, max tokens, context windows, and tool/function calling)Experience with agentic coding workflows or LLM-based code assistance, using tools that accelerate implementation, refactoring, and test generation while maintaining strong engineering rigor (reviews, testing, documentation, and CI discipline)Familiarity with server development, APIs, and containerization (Docker/Kubernetes)Strong problem-solving skills and comfortable writing production code—tests, docs, and allExcellent software engineering fundamentals: version control, testing, code review, documentationAbility to collaborate effectively in a team and work well under technical leadershipExcellent spoken and written English communication skillsWhat we consider a plus (not mandatory): Experience with manufacturing operations, MES systems, or Industry 4.0 conceptsFamiliarity with MLOps tools, model monitoring platforms, or ML infrastructureBasic knowledge of observability tools (Prometheus, Grafana, or similar) and data pipelinesProficiency in Python and experience with AI frameworks like PyTorch, TensorFlow, or LangChain Diversity, Equity and Inclusion are a source of commitment and innovation At Critical Manufacturing, we welcome and encourage applications from individuals of all backgrounds, regardless of disabilities, diverse abilities, identities, or experiences. Our commitment is to create an inclusive environment where everyone has equal opportunities to succeed and thrive. If you need accommodation during the recruitment process, please let us know—we're happy to support you.