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Machine Learning Engineer

Há 1 mês

Faro, Portugal Calyptus Tempo inteiro

Want to put your job search on autopilot? Join our platform, complete a 6-minute AI screening interview, and get auto-applied to 100s of high-paying roles. Sign up now at and let the opportunities come to you. ____________________________________________________________ What You’ll Do Lead the design and deployment of end-to-end ML systems for enterprise applications, from experimentation to production. Apply large language models (LLMs) effectively by: Fine-tuning and evaluating domain-specific models Developing robust prompt engineering and orchestration strategies Optimizing inference pipelines for latency, throughput, and cost efficiency Write production-quality software with strong engineering rigor, including clean APIs and reliable systems, while collaborating closely with product engineers. Build high-reliability ML infrastructure, including training pipelines, model registries, observability, and CI/CD for ML. Ensure ML solutions meet enterprise standards for security, compliance, data privacy (e.g., SOC2, GDPR), explainability, and auditability. Develop evaluation and monitoring frameworks to measure accuracy, fairness, robustness, and drift in deployed models. Partner with product and GTM teams to identify high-value enterprise use cases for ML and translate them into scalable solutions. Collaborate directly with customer-facing teams to deliver high-impact enterprise projects. Mentor engineers and raise the bar for technical excellence across the organization. Influence technical strategy and help define the company’s long-term AI roadmap. Who You’ll Be An experienced Python developer with strong knowledge of data structures, algorithms, and CI/CD pipelines. A Machine Learning professional skilled in data wrangling (SQL, pandas, NumPy), supervised and unsupervised learning, model evaluation, and feature engineering. Knowledgeable in Deep Learning frameworks such as PyTorch, with experience in neural networks and NLP models; exposure to generative and multi-modal models is a plus. Experienced in MLOps , including model serving, orchestration (Kubernetes), and experiment tracking, with the ability to design and deliver large-scale ML systems focused on cost optimization and reproducibility. Equipped with a solid foundation in linear algebra, probability, statistics, and calculus . An effective communicator who can translate technical concepts into clear business value and collaborate with non-technical stakeholders. A mentor and leader who provides guidance through code reviews, architectural decisions, and technical direction. ____________________________________________________________ Want to put your job search on autopilot? Join our platform, complete a 6-minute AI screening interview, and get auto-applied to 100s of high-paying roles. Sign up now at and let the opportunities come to you.