Machine Learning Engineer

Posted 28 days agoViewed

💎 Seniority level: Senior, At least 2 years

📍 Location: Italy

🔍 Industry: AI solutions for regulated industries

🗣️ Languages: English

⏳ Experience: At least 2 years

🪄 Skills: DockerPythonSQLGitMachine LearningNumpyPyTorchPandasTensorflow

A Master’s Degree or PhD in quantitative fields, like Computer Science, Engineering, Stats, Math, or Physics.
At least 2 years of proven experience as a Machine Learning Engineer (or a PhD).
Understanding of the fundamentals of statistics and machine learning.
Knowledge in at least one applied machine learning field.
Superb Python programming skills and experience with data science libraries.
Experience with deep learning models using TensorFlow or PyTorch.
Knowledge of version control systems and container tools.

Work side-by-side with Developers, Designers, and Data Scientists to understand customers’ needs.
Propose insightful solutions and implement/test machine learning models as efficient services integrated into the product ecosystem.
Explore state-of-the-art Machine Learning research to improve current products and develop new ones.
Continuously improve your scientific and engineering skills while mentoring others.

Posted 2 months ago

📍 Europe

🧭 Full-Time

🔍 Meeting management software

🏢 Company: SHERPANY

Strong background in ML and NLP with proven experience, preferably in self-hosted LLMs (e.g. LLaMA).
Proficiency in backend engineering, especially in Python and building scalable API integrations.
Skilled in Docker and Kubernetes/similar for model deployment within on-prem infrastructure.
Experience with data pipelines, logging, and monitoring for large-scale ML models.
Solution-oriented team player with proactive, autonomous work style.
Strong communication skills, both verbal and written, in English.

Develop, deploy, and optimize self-hosted ML pipelines focusing on LLM and NLP model performance.
Collaborate with backend, security, and DevOps teams to align LLM model architecture with infrastructure.
Design workflows for prompt templating, orchestration, and optimization of LLMs for product features.
Manage model performance monitoring, logging, and fine-tuning for continuous improvements.
Develop backend solutions that interface securely with ML models for efficient API performance and scaling.
Translate data science insights into actionable products.
Stay current with AI/ML developments and apply best practices.

DockerPythonKubernetesMachine LearningData science

Posted 2 months ago

🔧 Requirements