AI/ML Engineer

Lingaro is a company operating since 2008 with over 1500 talents across 7 global sites. It is a Great Place to Work® certified employer offering a diverse, inclusive, and values-driven community.

• At least 2+ years of Data engineering experience with at least 1 year in building Data processing.
• At least 2+ years of experience in production-ready Python code development (e.g., microservices, APIs).
• At least 1+ years of experience with GenAI (ChatGPT, Gemini, RAGs, prompt engineering).
• Practical experience in MLOps/LLMOps tools like AzureML/AzureAI.
• Practical experience with Databricks.
• Good understanding of ML/AI concepts: types of algorithms, machine learning frameworks, model efficiency metrics, model life-cycle, AI architectures.
• Good understanding of Cloud concepts and architectures, preferably Azure or GCP.
• Experience in at least one domain: Data Warehouse, Data Lake, Data Integration, Data Governance, Machine Learning, Deep Learning, MLOps.
• Practical experience in Spark/PySpark and Hive within Big Data Platforms like Databricks, EMR or similar.
• Experience in designing and implementing data pipelines.
• Good communication skills.
• Ability to work in a team and support others.
• Taking responsibility for tasks and deliverables.
• Great problem-solving skills and critical thinking.
• Fluency in written and spoken English.

Nice to have:

• Experience in designing, programming ML algorithms, and data processing pipelines using Python.
• Good understanding of CI/CD and DevOps concepts, experience with tools like GitHub Actions, GitLab, or Azure DevOps.
• Experience in productizing ML solutions using technologies like Spark/Databricks or Docker/Kubernetes.

• Working with Data Science teams to implement Machine Learning models into production.
• Design and delivery of GenAI solutions.
• Practical and innovative implementations of LLM/ML/AI automation for scale and efficiency.
• Design, delivery, and management of industrialized processing pipelines.
• Defining and implementing best practices in ML model life cycle and ML operations/LLM operations.
• Implementing AI/MLOps/LLMOps frameworks and supporting Data Science teams in best practices.
• Gathering and applying knowledge on modern techniques, tools, and frameworks in ML Architecture and Operations.
• Gathering technical requirements and estimating planned work.
• Presenting solutions, concepts, and results to internal and external clients.
• Creating technical documentation.