New opportunity

GenAI Sr. Developer

NTTD Info Pro Ser Pte Ltd · Remote, IN-KA, India

About this role

The GenAI Sr. Developer at NTT DATA will be responsible for developing and implementing AI/ML and generative AI solutions across various domains, including prediction systems, recommendation engines, text analytics, computer vision, chatbots, and content intelligence platforms. The role involves building and deploying advanced ML models, collaborating with cross-functional teams on end-to-end AI pipelines, and supporting the development of GenAI use cases, prototypes, and POCs.

Responsibilities

Develop and implement AI/ML and Generative AI solutions across various domains.
Build and deploy advanced ML models using frameworks such as TensorFlow, PyTorch, Keras, Spacy, and scikit-learn.
Apply deep knowledge of statistics, machine learning, NLP, and generative models including GANs and transformers.
Work with RAG (Retrieval-Augmented Generation) techniques.
Collaborate with cross-functional teams to design and implement end-to-end AI pipelines, from data ingestion to model deployment and monitoring.
Support the development of GenAI use cases, prototypes, and POCs tailored to specific industry requirements.
Communicate technical concepts clearly and effectively with both technical and non-technical stakeholders.
Engage with clients to understand business needs and deliver customized AI/ML solutions.
Stay current with trends and advancements in AI, GenAI, LLMs, and related fields.
Contribute to the development of frameworks, capabilities, and features for NTT DATA’s global GenAI platform and TechHub.

Required qualifications

Masters in AI from a reputed College / University.
8 years of demonstrated experience using Python and relevant packages for ML and data science in multiple real projects.
2 years of strong fundamentals in building GenAI solutions, preferably using Agentic frameworks.
2 years of deep knowledge in Predictive Analytics, Machine Learning, Deep Learning, Generative AI, Agentic systems, and MLOps / AI Infrastructure.
2 years of strong understanding of LLM technologies, embedding models, cloud-based AI services, and AI/ML operations.
2 years of hands-on experience with cloud-based AI platforms, including Azure OpenAI, Google Vertex Gen AI, and AWS AI services.
Strong problem-solving skills, critical thinking, and effective communication skills.

For full details about this role, click “Apply on company site” to view the employer’s original posting.

Skills for this role

PythonTensorFlowPyTorchKerasSpacyscikit-learnMachine LearningDeep LearningNatural Language ProcessingGenerative AIGANsTransformersRAGPredictive AnalyticsAgentic frameworksMLOpsAI InfrastructureLLMAzure OpenAIGoogle Vertex Gen AIAWS AI services

Checking your profile…

Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.

Understand the role

What matters most

End-to-End AI/ML Pipeline Delivery
The role requires managing the full lifecycle of AI solutions, from initial data ingestion to deployment and ongoing monitoring. You must demonstrate proficiency in building robust, scalable pipelines that integrate seamlessly into production environments using MLOps best practices.
Client-Facing Technical Consulting
As a senior developer, you are expected to engage directly with clients to translate complex business needs into actionable AI/ML solutions. Success depends on your ability to articulate technical concepts to non-technical stakeholders while ensuring the delivered prototypes meet specific industry requirements.
Advanced Generative AI and Agentic System Development
You must apply deep expertise in LLMs, RAG techniques, and agentic frameworks to build intelligent applications. This includes contributing to NTT DATA's global GenAI platform and staying ahead of rapid advancements in generative models.

Likely challenges to think through

Balancing Rapid Prototyping with Production Standards
You will frequently develop POCs and prototypes for diverse industry use cases. The challenge is maintaining high-quality, production-ready code standards while working in a fast-paced environment. Prepare by discussing your methodology for transitioning a successful POC into a stable, scalable enterprise deployment.
Multi-Cloud Platform Complexity
The role requires hands-on experience across Azure OpenAI, Google Vertex Gen AI, and AWS AI services. You may face difficulty in maintaining consistent performance and security standards across these disparate cloud ecosystems. Prepare by highlighting your experience in abstracting model deployment or managing multi-cloud AI infrastructure.
Translating Ambiguous Business Requirements
Delivering customized AI solutions for various domains requires interpreting vague client requests into concrete technical specifications. The difficulty lies in managing expectations while ensuring technical feasibility. Prepare by sharing examples of how you have successfully scoped AI projects by asking the right discovery questions.

Practice your answers

Technical practice

Designing RAG Pipelines

What to coverExplain your approach to building a Retrieval-Augmented Generation pipeline. Discuss how you handle document ingestion, vector database selection, embedding models, and the trade-offs between retrieval accuracy and latency.

Agentic Frameworks Implementation

What to coverDescribe your experience with agentic frameworks for GenAI. Discuss how you design agents to handle multi-step reasoning, tool usage, and error recovery in complex workflows.

MLOps and AI Infrastructure

What to coverDetail your strategy for managing the end-to-end lifecycle of an AI model, from data ingestion to deployment and monitoring. Focus on how you ensure model performance, drift detection, and scalability in production environments.

Cloud-Based AI Platform Proficiency

What to coverCompare the capabilities of Azure OpenAI, Google Vertex Gen AI, and AWS AI services. Discuss how you select the appropriate cloud platform based on specific project requirements like security, model availability, or cost.

Deep Learning and Transformer Architectures

What to coverDiscuss your experience applying transformer-based models to NLP tasks. Explain how you optimize these models for specific domains and the challenges you have faced with fine-tuning or prompt engineering.

Predictive Analytics vs. Generative AI

What to coverHow do you decide between a traditional predictive analytics approach and a generative AI solution for a business problem? Provide examples of when one is more suitable than the other regarding data requirements and expected outcomes.

Model Deployment and Frameworks

What to coverWhen building models with TensorFlow or PyTorch, how do you handle model versioning and containerization for deployment? Discuss your process for ensuring reproducibility across different environments.

Handling Data for AI Solutions

What to coverDescribe your methodology for data preprocessing and feature engineering in large-scale ML projects. Suggestion: Focus on how you ensure data quality and handle missing or unstructured data for both predictive and generative models.

Behavioural practice

Communicating Technical Concepts to Non-Technical Stakeholders

What to coverDescribe a situation where you had to explain a complex AI or GenAI concept to a non-technical client or stakeholder. How did you adapt your communication style to ensure they understood the business value and risks?

Collaborating in Cross-Functional Teams

What to coverProvide an example of a time you worked within a cross-functional team to deliver an end-to-end AI pipeline. How did you manage dependencies and ensure alignment between data engineers, product managers, and other developers?

Problem-Solving in Fast-Paced Environments

What to coverTell me about a time you encountered a significant technical roadblock while developing a prototype or POC. What steps did you take to troubleshoot the issue and what was the final result?

Staying Current with AI Advancements

What to coverThe field of GenAI evolves rapidly. Describe how you stay updated with the latest trends and research, and provide an example of how you have applied a new technique or tool to a real-world project.

Sharpen your knowledge

Topics to prepare

Agentic Frameworks and Systems
The role requires 2 years of experience in building GenAI solutions using Agentic frameworks. Review the conceptual architecture of autonomous agents, including planning, memory, and tool-use capabilities. Exercise: Design a high-level architecture for an agentic system that automates a multi-step business process, such as document analysis followed by automated report generation, and be prepared to discuss the trade-offs between different agent orchestration patterns.
Retrieval-Augmented Generation (RAG) Pipelines
RAG is explicitly listed as a core technique. Focus on the end-to-end pipeline, including document ingestion, chunking strategies, vector database selection, and retrieval optimization. Exercise: Map out a RAG pipeline for a domain-specific chatbot, identifying where latency or accuracy issues typically occur and how to mitigate them through advanced retrieval techniques like re-ranking or query expansion.
MLOps and AI Infrastructure
The position demands 2 years of experience in MLOps and AI infrastructure for production-grade deployments. Review the lifecycle of an AI model from ingestion to monitoring. Exercise: Describe a strategy for monitoring model drift and performance in a production environment, specifically focusing on how you would handle automated retraining or model versioning for a large-scale LLM application.

Skills to revise

Cloud-Based AI Platforms
The role requires 2 years of hands-on experience with Azure OpenAI, Google Vertex Gen AI, and AWS AI services. Exercise: Compare the specific capabilities of these platforms regarding fine-tuning, model hosting, and safety guardrails. Prepare to discuss a scenario where you would choose one provider over another based on specific enterprise requirements like data residency or cost.
Python for Advanced ML
With an 8-year requirement for Python in real-world projects, you must demonstrate proficiency beyond basic scripting. Exercise: Refactor a legacy ML pipeline script to improve modularity, type safety, and performance, ensuring it follows production-grade coding standards suitable for a global GenAI platform.
Deep Learning Frameworks
The role requires building and deploying models using TensorFlow, PyTorch, or Keras. Exercise: Select a complex model architecture (e.g., a Transformer-based model) and implement a custom training loop or a specialized data loader in PyTorch or TensorFlow to demonstrate your ability to handle non-standard model requirements.

Your preparation checklist

Prepare a portfolio of end-to-end AI pipelines
Document 2-3 professional projects where you managed the full lifecycle from data ingestion to deployment. Be ready to explain your choice of frameworks (TensorFlow, PyTorch, or scikit-learn) and how you handled MLOps and monitoring in a production environment.
Develop a technical case study on Agentic frameworks
Prepare a detailed walkthrough of a solution using Agentic frameworks or RAG techniques. Focus on how you addressed specific business requirements, the architecture of the LLM integration, and how you ensured the reliability of the generated output.
Map your cloud experience to specific platform services
Create a summary table of your hands-on experience with Azure OpenAI, Google Vertex Gen AI, and AWS AI services. Be prepared to discuss the trade-offs between these platforms for specific use cases like predictive analytics or content intelligence.
Practice translating technical AI concepts for stakeholders
Prepare two examples of how you explained complex AI/ML model performance or limitations to non-technical business clients. Focus on how you aligned technical outcomes with business value and project goals.
Formulate strategic questions for the interviewers
Prepare questions regarding NTT DATA's TechHub or global GenAI platform, such as how the team balances R&D with client-specific POC delivery, or how the organization approaches the governance and scaling of Agentic systems.

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