Note: The job is a remote job and is open to candidates in USA. NTT DATA is recruiting for a confidential client seeking a GenAI engineering lead aligned with a CIO organization. The role translates business priorities into production-grade AI solutions, advances enterprise AI platform adoption, and provides technical enablement and guidance.
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 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 such as Azure AI, AWS Bedrock, and internal platforms; translate 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 while acting as liaison between centralized AI platform and CIO engineering teams
Skills
- 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/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, 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
- Bachelor's degree in Computer Science, Engineering, Data Science, Artificial Intelligence, Machine Learning, or equivalent work experience
- 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
Benefits
- Company benefits, depending on the nature of the role offered, may include medical, dental, and vision insurance; a flexible spending or health savings account; life and AD&D insurance; short- and long-term disability coverage; paid time off; employee assistance; participation in a 401(k) program with company match; and additional voluntary or legally required benefits.
- Hybrid work model
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