GenAI Engineer

Job 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 with enterprise systems, APIs, and event-driven architectures. • Embed Responsible AI principles—fairness, explainability, transparency, and human-in-the-loop controls—into solution design and development. • Collaborate closely with Data Engineers, AI Architects, Security teams, and business stakeholders to deliver scalable, compliant AI solutions. • Provide engineering guidance, mentor junior team members, and contribute to reusable components, shared libraries, and engineering best practices. Requirements Technical Skills & Platforms • Strong hands-on experience building and deploying AI solutions on Azure, including Azure AI Foundry, Azure OpenAI, Azure Machine Learning, Azure AI Search, and Cognitive Services. • Solid understanding of machine learning concepts including feature engineering, model training, evaluation, hyperparameter tuning, and operational deployment. • Experience deploying both predictive ML and GenAI solutions in enterprise settings. Generative AI & Agent Systems • Hands-on experience with LLM-based system development, agent orchestration, and tool automation using frameworks such as: • LangChain • LangGraph • Semantic Kernel • MCP-style agent communication patterns • Experience implementing RAG pipelines, embeddings, vector databases, and document ingestion architectures. • Strong understanding of LLM constraints, prompt optimization, hallucination mitigation, and output‑validation strategies. MLOps, LLMOps & DevOps • Experience implementing CI/CD for ML and LLM workloads, including testing, monitoring, versioning, and automated deployment. • Familiarity with Azure DevOps pipelines, Git-based workflows, and cloud-native deployment automation. • Ability to balance rapid prototyping with strong engineering rigor, reliability practices, and production-readiness. Cloud, Security & Governance • Understanding of cloud-native patterns, containerization, and scalable AI infrastructure. • Knowledge of identity, access management, secrets management, and secure deployment practices for AI systems. • Familiarity with Responsible AI frameworks and enterprise governance models. Collaboration & Delivery • Ability to translate business problems into practical, scalable AI solutions. • Strong communication an