About this role.
This GenAI Engineer role involves designing, building, and deploying AI and ML solutions on Azure, with a focus on Generative AI, agent-based systems, and LLM-powered pipelines. The position requires strong technical expertise in Azure AI services, LangChain, RAG, and MLOps, along with collaboration across teams. It is a 1-year assignment with the possibility of extension, working remotely in LATAM or onsite in Washington, D.C. The ideal candidate will have hands-on experience deploying AI solutions in enterprise settings and mentoring junior team members. This role offers the opportunity to work with a global technology leader in a fast-evolving field.
Role DNA
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3/5Autonomy Level
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Core skills
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Cover letter sample
Dear Hiring Manager,
I am writing to express my strong interest in the GenAI Engineer position at NTT DATA. With extensive experience in designing and deploying AI solutions on Azure, including Generative AI, agent-based architectures, and RAG pipelines, I am confident in my ability to deliver scalable and secure systems. My background in MLOps and responsible AI aligns perfectly with the requirements outlined in the job description.
I have successfully built end-to-end ML solutions using Azure Machine Learning and have deep expertise in LangChain and Semantic Kernel for LLM orchestration. I am passionate about driving innovation and mentoring teams to achieve excellence in AI engineering.
Thank you for considering my application. I look forward to the opportunity to discuss how my skills can contribute to NTT DATA's success.
Sincerely,
[Your Name]
Sample interview questions
I built a RAG-based chatbot using Azure AI Search, Azure OpenAI, and Python. Challenges included optimizing token usage and latency. I used Azure Machine Learning for model deployment and integrated monitoring with Azure Monitor.
I design agents with a supervisor pattern, leveraging LangGraph for state management. For example, I built a customer support agent that routes queries to specialized sub-agents, using Semantic Kernel for planning and tool orchestration.
I set up CI/CD with Azure DevOps, automated model training and evaluation, implemented A/B testing, and monitored model drift using custom metrics and alerts.
I incorporate fairness and explainability by using Azure AI Content Safety, conducting bias checks, and implementing human-in-the-loop reviews for sensitive outputs.
I paired them on a RAG pipeline, explaining each component, and guided them through debugging. I emphasized best practices in coding, documentation, and testing.
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 and cross-functional collaboration skills.
- Experience working within Agile environments (Scrum, Kanban) delivering iteratively and incrementally.
Preferred Certifications & Training
- Databricks Certified Generative AI Engineer Associate
- Microsoft Azure AI Engineer Associate
- Azure Machine Learning Certification
- Azure Data Scientist Associate (optional)
- MLOps or LLMOps training
- LangChain/GenAI specialization coursework
Role Impact
This role is central to building and scaling enterprise-ready AI capabilities. It enables the development of secure, governed, high‑performing AI systems that support organizational innovation, automation, and decision intelligence.
Why This Opportunity Is Attractive
- Work with cutting-edge AI technologies and modern GenAI frameworks.
- Lead hands-on development of AI systems deployed at enterprise scale.
- Collaborate with cross-functional experts across architecture, engineering, and security.
Why NTT Data?
Empowerment and rewards are the cornerstone of our career development model. We are a young, fast-growing company, with a highly innovative and entrepreneurial spirit, because of this professional experience and growth will be unmatched. Our talent and positive attitude allow us to transform our goals into achievements, and projects into realities.
NTT Data is committed to hiring and retaining a diverse workforce. We are proud to be an Equal Opportunity/Affirmative Action-Employer, making decisions without regard to race, color, religion, creed, sex, sexual orientation, gender identity, marital status, national origin, age, veteran status, disability, or any other protected class. NTT Data is an Equal Opportunity Employer Male/Female/Disabled/Veteran and a VEVRAA Federal Contractor.
Annual salary information is not provided for this position. Explore salary ranges for similar roles in our Salary Directory ›
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