Senior ML Engineer (GCP) @ GetInData | Part of Xebia in GetInData | Part of Xebia

ML Engineers specializing in Generative AI are responsible for streamlining machine learning project lifecycles focused on Generative AI by designing and automating workflows, implementing CI/CD pipelines, ensuring reproducibility, and providing reliable experiment tracking. Their work mainly focuses on building Generative AI applications based on state-of-the-art Generative AI models by integrating open-source models or third-party commercial services offering Generative AI models as a service. They are not responsible for the Generative-AI models training but for integrating the best-in-class tools into production-level applications. They collaborate with stakeholders and platform engineers to set up infrastructure, automate model deployment, and monitor models. ML Engineer - Generative AI possesses a wide range of technical skills, including knowledge of orchestration, storage, containerization, observability, SQL, programming languages, cloud platforms, and data processing. Their expertise also covers techniques related to Information Retrieval (IR), Natural Language Processing, semantic search, and vector databases. Bachelor’s or Master’s degree in Computer Science, Data Science, or a related field Extensive experience in data engineering, including working with BigQuery, Airflow Commercial experience in text processing, like calculating embeddings, configuring free-text or semantic search systems, using technologies like Elasticsearch, Langchain, LlamaIndex, or similar Solid experience working with VertexAI Proficiency in Python Familiarity and experience with commercial and/or open-source LLMs Familiarity with GCP environment Solid understanding of machine learning and AI principles Ability to actively participate/lead discussions with clients to identify and assess concrete and ambitious avenues for improvement ML Engineers specializing in Generative AI are responsible for streamlining machine learning project lifecycles focused on Generative AI by designing and automating workflows, implementing CI/CD pipelines, ensuring reproducibility, and providing reliable experiment tracking. Their work mainly focuses on building Generative AI applications based on state-of-the-art Generative AI models by integrating open-source models or third-party commercial services offering Generative AI models as a service. They are not responsible for the Generative-AI models training but for integrating the best-in-class tools into production-level applications. They collaborate with stakeholders and platform engineers to set up infrastructure, automate model deployment, and monitor models. ML Engineer - Generative AI possesses a wide range of technical skills, including knowledge of orchestration, storage, containerization, observability, SQL, programming languages, cloud platforms, and data processing. Their expertise also covers techniques related to Information Retrieval (IR), Natural Language Processing, semantic search, and vector databases. ,[Designing and architecting machine learning workflows / machine learning lifecycle process, Collaborating with Platform Engineers to setup the infrastructure required to run MLOps processes efficiently, Implementing knowledge-retrieval systems, semantic search, or vector stores using e.g. Elasticsearch, Building AI-based applications like conversational search, recommendation systems or chatbots, Collaborating with stakeholders to understand the main pain points and inefficiencies of Machine Learning project lifecycles within the company, Keeping abreast of the latest trends and advancements in data engineering, machine learning, and AI] Requirements: Python, BigQuery, GCP, Vertex AI Tools: Jira, GIT, GitLab, Jenkins / GitLab, Agile. Additionally: Sport subscription, Private healthcare, Flat structure, Small teams, International projects, Team Events, Training budget, Free coffee, Gym, Bike parking, Playroom, Free snacks, Free beverages, In-house trainings, Startup atmosphere, No dress code, Kitchen.