AI Engineering Associate Director

NTT DATA Services•Plano, TX

4d•Remote

About The Position

NTT DATA is seeking an AI engineering professional to design, build, test, and deploy artificial intelligence, machine learning, and generative AI solutions across industries, primarily in healthcare, life sciences, and the public sector. The role partners with solution architects, industry subject-matter experts, and client stakeholders to turn AI opportunities into scalable, secure, reusable solutions aligned with business outcomes. The position combines hands-on engineering across AI applications, data pipelines, model integration, cloud services, APIs, automation, and modern software engineering with client delivery and the development of internal accelerators, reusable assets, proofs of concept, demos, and technical content. The engineer is expected to move from experimentation toward production-minded solutions while balancing speed, technical quality, responsible AI practices, and business value. This is a remote role located in the United States. NTT DATA notes that remote or hybrid arrangements may change based on client requirements, and employees near an office or client site may need to attend meetings or events in person depending on business needs.

Requirements

Bachelor’s degree in computer science, information systems, engineering, data science, analytics, or a related field, or equivalent practical experience.
At least 6 years of experience in software engineering, data engineering, AI engineering, machine learning engineering, analytics engineering, or technical consulting.
Hands-on experience building AI, machine learning, generative AI, analytics, automation, or data-driven applications.
Practical experience with generative AI patterns such as prompt engineering, retrieval-augmented generation, embeddings, vector databases, LLM integration, model evaluation, and API-based model orchestration.
Proficiency in Python and SQL, with experience using common data, AI, and application development libraries or frameworks.
Experience with at least one major cloud platform, such as Azure, AWS, or Google Cloud.
Experience with APIs, databases, data pipelines, structured and unstructured data, and enterprise integration patterns.
Familiarity with software engineering practices such as Git, CI/CD, testing, code review, documentation, containerization, and deployment.
Ability to build technically credible solutions and communicate clearly with technical and non-technical audiences.
Strong collaboration skills and ability to work with cross-functional teams in consulting, client delivery, product, or enterprise technology environments.

Responsibilities

Design, develop, and implement AI and generative AI solutions, primarily for healthcare, life sciences, and public sector domains.
Build AI-enabled applications, services, APIs, copilots, chat interfaces, workflow automations, dashboards, and decision-support tools.
Develop solutions using AI engineering patterns such as retrieval-augmented generation, embeddings, vector search, prompt engineering, model orchestration, agentic workflows, natural language processing, document intelligence, predictive analytics, and machine learning.
Partner with solution architects, data scientists, cloud engineers, software developers, and business stakeholders to translate requirements into technical designs and working solutions.
Build and maintain data pipelines, integration services, feature preparation workflows, knowledge bases, and model-ready datasets.
Integrate AI models and services from major cloud providers, commercial model providers, open-source frameworks, and enterprise platforms.
Create proofs of concept, prototypes, demos, and technical assets to validate feasibility, demonstrate business value, and support client conversations.
Support the sales cycle through demo environments, technical walkthroughs, proposal content, estimates, and reusable solution components.
Develop reusable accelerators, code libraries, templates, prompt assets, deployment patterns, and implementation playbooks.
Apply responsible AI practices, including model evaluation, guardrails, privacy controls, bias testing, explainability, monitoring, auditability, and human-in-the-loop design.
Collaborate with security, privacy, legal, compliance, and architecture teams to ensure solutions meet enterprise and client requirements.
Participate in testing, debugging, performance tuning, deployment, documentation, and operational handoff.
Stay current on emerging AI technologies, frameworks, tools, and engineering practices, and translate relevant innovations into practical solutions.