GenAI Engineer
NTT DataJob Title: GenAI Engineer
Location Preference: 100% remote in Mexico, Brasil, Peru, Chile working EST Time Zone OR onsite in Washington, D.C.
Duration: 1-Year Assignment with possibility of extension
NTT DATA is a team of more than 139,000 diverse professionals operating in more than 50 countries worldwide. Our sectors of activity include telecommunications, finance, industry, utilities, energy, public administration, and health.
Our mission? Offer technological solutions, business, strategy, development, and application maintenance while being a benchmark in consulting. Thanks to the collaboration between teams, the human quality of our people, and the fact that we do not conform to what is established, we always seek innovation that brings us closer to the future.
Our essence has led us to the forefront of technology, breaking paradigms and providing solutions that truly respond to each client's needs. Our talent has led us to be one of the top six technology companies in the world.
Because #Greattech, needs #GreatPeople, like you
NTT Data seeks high-achieving team players who quickly adapt to new challenges and entrepreneurial ventures. We are looking fora GenAI Engineer to work with our global client for a fully remote opportunity in LATAM working EST hours.
Position Summary
The GenAI Engineer is a core technical contributor responsible for designing, building, deploying, and managing AI and Machine Learning solutions across enterprise environments. This role focuses on implementing both classical ML and modern Generative AI workloads, including agent-based systems, Retrieval-Augmented Generation (RAG), and LLM-driven pipelines.
The engineer ensures all AI solutions are scalable, secure, governed, and aligned with enterprise architecture and operational requirements.
Key Responsibilities
- Design, build, and deliver end-to-end AI/ML solutions—from experimentation and prototyping to production deployment.
- Develop AI solutions using Azure AI Foundry, Azure OpenAI, Azure Machine Learning, and related Azure AI services.
- Build agent-based architectures using frameworks such as LangChain, LangGraph, Semantic Kernel, and MCP-style orchestration patterns.
- Design and optimize prompt engineering strategies, RAG pipelines, embeddings, vector search, and knowledge-grounding workflows.
- Build, train, evaluate, and deploy classical ML and GenAI models using Azure Machine Learning, including pipelines, feature engineering, model registry, and experiment tracking.
- Implement MLOps and LLMOps practices including CI/CD, automated testing, responsible deployment, model monitoring, drift detection, and performance optimization.
- Integrate AI solutions securely
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