Job Opportunities API

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, Life Sciences, and Industrial Equipment. 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. We value people who are as skeptical as they are curious, who test claims before betting the roadmap on them. 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 sources Optimize server performance and define clear interfaces for tool integration, ensuring models have safe, reliable access to business logic Collaborate with team leads to map complex manufacturing workflows into structured tools and prompts Build Model Observability and Telemetry Infrastructure Design and implement comprehensive telemetry systems to track model behavior, token usage, latency, and cost in production Create dashboards and alerting systems that give real-time visibility into model performance and anomalies Instrument models to capture structured traces: prompts/system context, tool invocations, inputs/outputs, intermediate artifacts, and decision metadata Contribute to standards for logging, tracing, and distributed observability across all AI systems Develop Retraining and Continuous Improvement Pipelines Build data collection pipelines that capture production interactions, model failures, and edge cases for retraining Implement automated systems for evaluating model improvements and managing safe rollouts Contribute to feedback loops that allow the platform to learn from real-world usage without manual intervention Support Team Deliverables Write clean, testable code and contribute to team codebases, documentation, and CI/CD processes Participate in code reviews, technical design reviews, and troubleshooting production issues Experiment with new tools and techniques under team guidance to improve AI system reliability Promote the adoption of agentic coding across teams to accelerate delivery and increase throughput while maintaining quality and security standards Design repositories, CI, and developer tooling that make agent-driven changes safe (linting, typed APIs, contract tests, golden tests, eval gates) Ensure Production Reliability Implement robust error handling, fallback strategies, and graceful degradation for AI systems Monitor and tune AI systems for performance, uptime, and safety in manufacturing environments Gather feedback from operations and product teams to refine tooling and server implementations What Success Looks Like Within your first year, you will have:&nb