NTT DATA strives to hire exceptional, innovative and passionate individuals who want to grow with us. If you want to be part of an inclusive, adaptable, and forward-thinking organization, apply now.
We are currently seeking an AI Engineer (R&D) to join our team in Dallas, Texas (US-TX), United States (US).
Looking for experienced AI Engineer to support the design, development, and delivery of AI-enabled data solutions within Research & Development (R&D). This is a highly hands-on role focused on building production-grade data applications and analytics that directly enable R&D decision-making.
Will work closely with R&D stakeholders and Digital, Data, and AI partners to translate real business needs into scalable, secure, and maintainable data products. This role is ideal for an engineer who codes daily, leverages AI-assisted development tools effectively, and sets a high bar for technical quality through strong code review and engineering standards.
Core responsibilities:
Design, build, and maintain scalable R&D data capture, ingestion, and analytics systems supporting structured and semi-structured data across the R&D lifecycle
Develop production-grade Python and SQL code for data pipelines, AI-enabled analytics, and automation with a strong focus on performance, reliability, and maintainability
Leverage AI-assisted coding tools to accelerate delivery while ensuring solutions meet Client's security, data privacy, and quality standards
Translate R&D and business requirements into fully functional data products and applications, not just proofs of concept
Create interactive front-end prototypes (wireframes, lightweight apps, or functional mock-ups) to validate user workflows and reduce delivery risk
Provide technical leadership through architecture input, code-level guidance, and rigorous peer and AI-generated code reviews
Lead end-to-end User Acceptance Testing (UAT), including scenario design, edge-case validation, and production readiness sign-off
Provide post-deployment hypercare and aftercare, including monitoring, issue triage, bug fixes, access management, and data quality checks
Evaluate third-party AI platforms and tools, assessing technical fit, scalability, cost, and alignment with Client IT and AI governance standards
Communicate progress, risks, and design decisions clearly to both technical and non-technical stakeholders
Qualification:
Bachelor's degree in Computer Science, Information Technology, Data Science, or a related field
7+ years of strong, hands-on experience with Python for data engineering and analytics, including modular design, logging, configuration management, and automation
5+ years of advanced SQL expertise, including query optimization and working with large, complex datasets
Proven experience designing and optimizing data models that balance performance, usability, and analytics needs
5+ years of experience with cloud-based data platforms such as Databricks, Delta Lake, or equivalent technologies, including performance and cost optimization
3 to 5 years of demonstrated success building and launching applications or data products using AI-assisted coding tools
Ability to critically assess, refactor, test, and productionize AI-generated code to enterprise standards
Extensive experience with Git-based workflows, including branching strategies, pull requests, and peer code reviews
Strong communication skills with the ability to translate technical concepts and AI outcomes into clear, actionable insights
Highly self-directed, delivery-focused, and comfortable working in fast-moving, evolving data and AI environments
Nice to have:
Experience supporting R&D, manufacturing, supply chain, or scientific data environments
Exposure to statistics, Design of Experiments (DOE), or advanced analytics workflows
Experience building internal data tools or reusable analytics frameworks
Common Expectation from all the roles below:
Compliance with Client's responsible AI principles and Acceptable Use policy
Adherence to data residency, privacy (GDPR, HIPAA where applicable), and 21 CFR Part 11 controls where in scope
Third-party risk assessment and SOC 2 Type II (or equivalent) certification
Disclosure of subcontractors and offshore delivery locations
Disclosure of model providers, training data practices, and any use of client data for model improvement (opt-out required)