Machine Learning Engineer / AI Engineer

NTT DATA is seeking a GenAI/AI engineering professional to partner with a CIO organization and deliver production-grade solutions across LLMs, RAG, and agentic AI patterns. The role focuses on scalable implementation, secure and observable workflows, and technical enablement for enterprise adoption.

Location and Work Model

• Charlotte, NC (onsite)

Salary

USD 65 to 80 per hourly. The starting pay range for this role is [$65.67 to $80.11].

Role Summary

Design, develop, and deploy production-grade GenAI solutions using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and agentic AI patterns. Support enterprise AI platform adoption, implement secure and observed workflows, and provide technical training and guidance through workshops and design reviews.

Key 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, 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 such as Azure AI, AWS Bedrock, and internal platforms by translating platform capabilities into practical implementation patterns.
• Design secure RAG and agentic workflows, including document ingestion, embeddings, vector search, orchestration frameworks, tools/APIs, enterprise data sources, and guardrails.
• Ensure alignment with 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 while serving as a liaison between the centralized AI platform and CIO engineering teams.

Required Qualifications

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

Education

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

Technologies

Python, Large Language Models (LLMs), Retrieval-Augmented Generation (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.

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.