ML Engineer Senior - GenAI Solutions

As a Senior ML Engineer at NTT DATA, you will collaborate with data scientists and ML engineers on advanced Generative AI initiatives. You apply hands-on GenAI capabilities across cloud and on-prem architectures, shaping scalable data pipelines and automated processes. You will lead model development, validation, and production deployment while identifying business opportunities. This role sits at the intersection of data, software, and client engagement, offering a chance to mentor colleagues and influence cross-functional projects.

• Apply Generative AI capabilities in cloud (Azure/GCP) and on-prem GenAI architectures and MLOps
• Work on classification, information retrieval, clustering and optimization problems
• Build scalable, automated data analysis processes for large-scale data
• Contribute to model development, validation and production implementation
• Identify business opportunities and connect with stakeholders
• Design and create data pipelines from experiments to production
• Manage multiple projects and lead ML Engineers

• 5+ years of production experience in Data Science or Software Engineering
• Solid foundation in math, probability, statistics and algorithms
• 6–12 months of Generative AI deployment experience and underlying architecture
• Vector Database knowledge is a plus
• Understanding of data structures, data modeling and software architecture
• Fluent in at least two programming languages among Python, Scala, Java, C++
• Experience building infrastructure for data scientists/ML practitioners
• Strong knowledge of Spark; Databricks is a plus
• Experience deploying ML solutions in cloud and cross-cloud environments; Snowflake knowledge is a plus
• Deep knowledge of ML frameworks (Keras, PyTorch)
• Ability to design/implement production ML pipelines; familiarity with CI/CD, containerization, and MLops/Re-Training/Drift Management
• Ability to mentor junior team members and maintain relationships with teams and clients

• leadership and mentoring
• stakeholder management
• cross-functional collaboration
• Generative AI deployment
• Azure and Google Cloud Platform (GCP)
• on-prem GenAI architectures