Machine Learning

Há 3 dias

Lisboa, Portugal Skillspark AB Tempo inteiro

**Start**

**ASAP**

**Duration**

**10-12 months**

Contributed to the design and development of AI-driven digital products supporting sustainable and connected transportation for a leading global commercial vehicle manufacturer. Played a key role in building machine learning solutions with a strong focus on MLOps, scalability, and real-world impact.

**Key Responsibilities**:
- Led the end-to-end development of an AI-based lead generation system, integrating **Large Language Models (LLMs)** and **Computer Vision** techniques to automatically extract and interpret data from heterogeneous sources.- Built a web-based interface using **FastAPI** to facilitate early validation with business stakeholders, followed by successful deployment on **Azure Web App** for production use.- Contributed to the advancement of **Generative AI** and **Vision-Language Models**, enhancing their effectiveness for internal marketing and sales automation use cases.- Designed, trained, and evaluated **machine learning models** using **Python**, **PyTorch**, and **Transformers** for image classification, text generation, and multimodal tasks.- Implemented **MLOps workflows** leveraging **Azure Machine Learning**, **Databricks**, and **MLflow**, reducing time-to-deployment and improving reproducibility.- Built robust data and ML pipelines using **Databricks Asset Bundles (DABs)** to ensure safe and structured promotion between dev, staging, and production environments.-
- Created complex, modular **Databricks Workflows** to orchestrate data ingestion, model training, evaluation, and inference jobs with improved observability and reliability.- Optimized data processing pipelines using **PySpark**, achieving substantial gains in performance and scalability for large-scale datasets.

**Technologies & Tools**:
**Languages & Frameworks**: Python, PyTorch, FastAPI, PySpark**AI/ML**: Transformers, Hugging Face, OpenAI API, Vision Models, LLMs, Generative AI**MLOps & Data Engineering**: Databricks, MLflow, Azure Machine Learning, Azure Web App, GitHub Actions, Databricks Asset Bundles (DABs), Databricks Workflows**Cloud Platforms**: Microsoft Azure (Web App, Storage, Compute, Key Vault)**Other**: CI/CD, REST APIs, Docker (optional if used), Agile development

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