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
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.
