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Senior Machine Learning Engineer – Industrial Innovation & Generative AI

Lisboa

Job description

We are looking for a Senior Machine Learning Engineer to design and deliver production-grade AI components that automate complex operational workflows in industrial and infrastructure contexts. You will build solutions spanning document intelligence (OCR + LLMs), agentic automation, LLM-based reasoning over rules and policies, and MLOps/observability — with a strong focus on reliability, evaluation, and scalable deployment.

This role suits someone who enjoys bridging applied research with engineering execution: rapid prototyping, measurable experiments, and turning successful ideas into robust systems used across large portfolios of real-world assets and sites.

What are the responsibilities?

Lead the design, development, and deployment of advanced analytics components within industrial systems, with a focus on Generative AI, LLMs, computer vision, and signal processing.
Build production-ready document intelligence pipelines to extract, validate, and structure data from semi-structured documents (e.g., invoices, statements, forms) using OCR + LLM workflows, including quality checks and exception handling.
Develop LLM-powered “policy/rule reasoning” services that interpret complex eligibility/contract/tariff-like rules and match them against site/customer profiles to recommend optimal options and outcomes.
Design and implement agentic workflows for scalable information discovery and data acquisition (e.g., automating collection of public/partner data sources), including tooling, safety constraints, and evaluation.
Create orchestration, monitoring, and cost controls for GenAI systems (tracing, latency/error monitoring, token usage/cost, standardized logging/alerts) to ensure production reliability.
Own end-to-end delivery: from feasibility studies and prototypes to deployment-ready services (APIs, batch jobs, workflows), collaborating with software engineers, platform/MLOps, and domain experts.
Establish evaluation methodology for LLM/agent systems (gold datasets, accuracy metrics, regression tests, offline/online evaluation, failure mode analysis) and drive iterative improvement.
Mentor and lead technically, contributing to engineering standards, reusable templates, and knowledge sharing across teams.
Contribute to innovation outputs (internal tech talks, invention disclosures/patents, publications where relevant)

Requirements

Senior Level - Master’s or PhD in Computer Science, Data Science, Mathematics, Engineering, or a related field.
Senior Level - 7+ years building ML/AI systems in industrial, infrastructure, or enterprise settings, including taking solutions from prototype to production.
Senior Level - Strong Python engineering skills and experience with ML/AI libraries and modern software practices (testing, packaging, APIs, CI/CD).
Senior Level - Proven hands-on work with LLMs / Generative AI in applied settings, including prompt design, tool-using agents, and retrieval/grounding patterns (RAG).
Senior Level - Experience building information extraction / document understanding solutions (OCR pipelines, structured outputs, validation, human-in-the-loop flows).
Senior Level - Practical knowledge of workflow orchestration and production pipelines (e.g., Airflow-like orchestration, job scheduling, dependency management).
Senior Level - Strong understanding of observability for AI systems (logging, tracing, evaluation, monitoring, and cost governance for LLM usage).
Senior Level - Comfortable collaborating with stakeholders to translate ambiguous domain problems into measurable ML deliverables and robust services
Senior Level - Experience in utilities / energy / building operations / infrastructure analytics, especially where cost optimization and rules-driven decisions matter. Prior work in time-series/signal processing or industrial monitoring, including anomaly detection and fault diagnostics.
Senior Level - Computer vision experience (industrial or remote sensing), active learning, few-shot adaptation.
Senior Level - Demonstrated innovation track record (patents / invention disclosures / technical publications)

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