3 months ago
Lisbon, PortugalMid Level
Responsibilities
- Design and build scalable ML pipelines for unstructured data such as documents, transcripts, images, logs, and free-form text.
- Develop and maintain production AI services and APIs for internal applications and analytics platforms.
- Productionize ML and Generative AI solutions through packaging, containerization, deployment, versioning, and lifecycle management.
- Build RAG, embedding, semantic search, and LLM-powered API workflows.
- Implement monitoring, observability, model performance tracking, and SLA compliance measures.
- Collaborate with Data Scientists to transition experimental models into stable production environments.
- Contribute to CI/CD, MLOps, automated testing, deployment automation, retraining workflows, and infrastructure standardization.
- Translate business needs into scalable AI-enabled solutions and document systems and technical decisions.
Requirements
- At least 3 years of professional experience in ML Engineering, Applied AI, Data Science, or related engineering roles focused on production systems.
- Hands-on experience with unstructured-data solutions, including NLP pipelines, document processing, OCR, text classification, embeddings, entity extraction, or semantic search.
- Proficiency in Python and SQL, with experience using PySpark and Databricks or similar distributed compute platforms.
- Experience developing and deploying production APIs for ML model serving using FastAPI or similar frameworks.
- Familiarity with MLOps, CI/CD for ML, model monitoring, automated retraining, Docker, and Git-based workflows.
- Experience with cloud-based AI/ML platforms and enterprise deployment environments.
- Strong software engineering fundamentals, including readable code, testing, debugging, and performance optimization.
- Preferred experience with OpenAI, Azure OpenAI, or open-source models; LangChain or LlamaIndex; and vector databases or retrieval platforms such as Azure AI Search, Pinecone, or pgvector.
- Exposure to responsible AI practices such as explainability, bias detection, governance, and model risk management.
Benefits
- Hybrid work arrangement with 60% of working time from the Lisbon office.
- Competitive compensation, comprehensive benefits, and a flexible work environment supporting work-life balance.
- Diverse, inclusive, collaborative culture with continuous learning, development, growth, and advancement opportunities.
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