Machine Learning Engineer / AI Engineer

NTT DATA Services•Charlotte, NC

About The Position

NTT DATA is seeking a Machine Learning Engineer / AI Engineer to partner with an assigned CIO organization as its primary Generative AI engineering lead. The role translates business priorities into scalable AI solutions and provides hands-on engineering to design, develop, test, and deploy production-grade solutions using large language models, retrieval-augmented generation, and agentic AI patterns. The engineer will accelerate high-value use cases through rapid prototyping, reusable components, reference architectures, and production solutioning. The role supports adoption of enterprise AI platforms such as Azure AI, AWS Bedrock, and internal platforms, and designs secure workflows incorporating document ingestion, embeddings, vector search, orchestration frameworks, tools and APIs, enterprise data sources, and guardrails. Solutions must meet enterprise architecture, security, privacy, responsible AI, governance, observability, and production support standards. The position also delivers workshops, training, design reviews, and technical guidance; creates documentation, playbooks, quick-start guides, sample code, and best practices; and acts as a liaison between centralized AI platform and CIO engineering teams. The location is listed as Charlotte, North Carolina, and travel is required as business and engagement needs dictate. The posting also describes the role as remote in its compensation statement.

Requirements

At least 8 years of overall software engineering, data engineering, machine learning engineering, or AI solution development experience in enterprise environments.
At least 5 years of hands-on experience designing and delivering machine learning, AI, or data-driven applications using Python and modern software engineering practices.
At least 3 years of hands-on experience building Generative AI / LLM applications, including prompt engineering, model integration, evaluation, and production deployment.
At least 3 years of experience designing and implementing RAG solutions using embeddings, vector search/databases, document processing, retrieval techniques, and grounding patterns.
At least 2 years of experience developing agentic AI or multi-step AI workflows using orchestration frameworks, tool/function calling, APIs, and enterprise integrations.
At least 3 years of experience with cloud AI/ML platforms, including Azure AI / Azure OpenAI, AWS Bedrock, or comparable enterprise AI services.
At least 3 years of experience with cloud-native engineering, CI/CD, containers, observability, security controls, and production deployment practices.
At least 2 years of experience in technical enablement, architecture, consulting, or engineering leadership, including workshops, mentoring, reusable guidance, and application of responsible AI/governance requirements.
Bachelor's degree in Computer Science, Engineering, Data Science, Artificial Intelligence, or Machine Learning, or equivalent work experience.

Nice To Haves

Experience with LangChain, LangGraph, Semantic Kernel, LlamaIndex, AutoGen, or comparable LLM / agent orchestration frameworks.
Experience with vector technologies such as Azure AI Search, OpenSearch, Pinecone, pgvector, FAISS, or equivalent platforms.
Experience with LLMOps / MLOps, prompt and model evaluation, AI observability, guardrails, red-teaming, and production quality measurement.
Experience building reusable GenAI accelerators/reference architectures or delivering AI solutions in highly regulated enterprise environments such as financial services.

Responsibilities

Partner with an assigned CIO organization as the primary GenAI engineering lead and translate business priorities into scalable AI solutions.
Design, develop, test, and deploy production-grade GenAI solutions using LLMs, RAG, and agentic AI patterns.
Accelerate high-value use cases through hands-on engineering, rapid prototyping, reusable components, reference architectures, and production solutioning.
Drive adoption of enterprise AI platforms such as Azure AI, AWS Bedrock, and internal platforms, translating platform capabilities into practical implementation patterns.
Design secure RAG and agentic workflows using document ingestion, embeddings, vector search, orchestration frameworks, tools/APIs, enterprise data sources, and guardrails.
Ensure solutions meet enterprise architecture, security, privacy, responsible AI, governance, observability, and production support standards.
Deliver workshops, training, design reviews, and technical guidance on prompt engineering, RAG, orchestration, LLM application patterns, and platform usage.
Develop reusable documentation, playbooks, quick-start guides, sample code, and best practices.
Act as liaison between centralized AI platform and CIO engineering teams.