Software Engineer Lead - Machine Learning Engineer

at  Capgemini

JOB DESCRIPTION

As a Machine Learning Engineer, you will lead the development and implementation of advanced data engineering solutions to support the deployment and optimization of AI models. Your role will involve leveraging your extensive experience to design robust, scalable, and innovative data architectures that align with the unique requirements of Artificial Intelligence applications.

REQUIRED SKILLS

• Bachelor’s degree in computer science, data engineering, or a related field with 5+ years experience (Master’s preferred).
• Proven experience in data engineering, ETL, and database management.
• Proficiency in SQL and data manipulation languages.
• Proven experience deploying solutions in Azure
• Strong programming skills, with knowledge of languages like Python, Java, or Scala.
• Experience with data warehousing platforms (e.g., Amazon Redshift, Snowflake) and big data technologies (e.g., Hadoop, Spark).
• Experience with database technologies for structured and unstructured data both for storage and optimal retrieval
• Experience with highly scalable Data stores, Data Lake, Data Warehouse, Lakehouse, and unstructured datasets
• Familiarity with data modeling and schema design.
• Knowledge of data integration tools and data orchestration.

• The Machine Learning Engineer will be responsible for architectural design and planning, advanced data pipelines, model integration and optimization, scalability, performance and research and innovation supporting production AI systems.
• Build and maintain data engineering solutions on cloud platforms using hyperscaler services.
• Design, develop, and maintain data pipelines to efficiently collect, process, and load data from various sources into data storage systems (e.g., data warehouses, data lakes).
• Strong understanding of fundamental data science concepts in NLP, including selection and understanding of embedding models.
• Develop and maintain data models and schema designs to support efficient data storage and retrieval.
• Use hyperscaler technologies to support data needs for expansion of Machine Learning/Data Science capabilities including generative AI.
• Implement data validation and data cleansing processes to ensure data quality and consistency.
• Design, develop, and implement scalable data pipelines and ETL/ELT processes using Python, PySpark and API integrations.
• Collaborate with cross-functional teams to understand data requirements and integrate data into various applications and analytics platforms.
• Optimize data systems for scalability and performance, anticipating and addressing potential bottlenecks.