Machine Learning Engineer / AI Engineer

NTT DATA is seeking a GenAI-focused Machine Learning Engineer / AI Engineer to support an assigned CIO organization in delivering production-grade AI solutions. In this onsite role in Charlotte, North Carolina, you will design and deploy scalable GenAI capabilities using LLMs, RAG, and agentic AI patterns, while also helping teams adopt enterprise AI platforms and secure, observable workflows.

What you will do

• Serve as the primary GenAI engineering lead for a CIO organization, translating business priorities into scalable AI solutions.
• Design, develop, test, and deploy production-grade GenAI solutions using Large Language Models (LLMs), Retrieval-Augmented Generation (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, including Azure AI and AWS Bedrock, translating platform capabilities into practical implementation patterns.
• Design secure RAG and agentic workflows covering document ingestion, embeddings, vector search, orchestration frameworks, tools/APIs, enterprise data sources, and guardrails.
• Ensure solutions align with enterprise architecture, security, privacy, responsible AI, governance, observability, and production support standards.
• Provide technical enablement through workshops, training, design reviews, and guidance on prompt engineering, RAG, orchestration, LLM application patterns, and platform usage.
• Create reusable documentation, playbooks, quick-start guides, sample code, and best practices, acting as a liaison between centralized AI platforms and CIO engineering teams.

Required qualifications

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

Technologies

• Python, LLMs, RAG, agentic AI patterns
• Azure AI, AWS Bedrock, Azure OpenAI
• CI/CD, containers, observability
• Embeddings, vector search, vector search/databases, document ingestion
• Orchestration frameworks, tools/APIs, enterprise data sources, guardrails
• Prompt engineering, model integration, evaluation
• Enterprise architecture, security, privacy, responsible AI, governance
• Azure AI Search, OpenSearch, Pinecone, pgvector, FAISS
• LangChain, LangGraph, Semantic Kernel, LlamaIndex, AutoGen
• LLMOps / MLOps, red-teaming

Education

Bachelor's degree in Computer Science, Engineering, Data Science, Artificial Intelligence, Machine Learning, or equivalent.

Benefits

• Medical, dental, and vision insurance
• Flexible spending or health savings account
• AD&D insurance
• Employee assistance
• Participation in a 401k program
• Additional voluntary or legally-required benefits

Nice to have

• 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.

Travel: Travel as required based on business and CIO-aligned engagement needs.

Compensation: The starting pay range for this role is $65.67 to $80.11 per hour.