Lead AI Consultant

Req ID: 383339

NTT DATA strives to hire exceptional, innovative and passionate individuals who want to grow with us. If you want to be part of an inclusive, adaptable, and forward-thinking organization, apply now.

We are currently seeking a Lead AI Consultant to join our team in Bangalore, Karnātaka (IN-KA), India (IN).

Job Title

Lead AI Engineer (LLM / NLP / Multi-Agent Systems)

Location

Bangalore, India (Hybrid)

Employment Type

Full-Time

Experience Required

• 10+ years of overall experience in Software Engineering and Technology Solutions.
• 5+ years of hands-on experience in Machine Learning, Artificial Intelligence, and Natural Language Processing (NLP).
• 2+ years of experience designing and developing LLM-powered applications and Generative AI solutions.
• 1.5+ years of experience building and deploying Multi-Agent AI Systems in production environments.
• Proven track record of delivering scalable AI/ML solutions on cloud platforms.

About the Role

We are seeking an experienced and innovative Lead AI Engineer to drive the design, architecture, and implementation of next-generation AI platforms powered by Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and Multi-Agent AI Systems.

This is a hands-on technical leadership role requiring deep expertise in Machine Learning, NLP, Generative AI, distributed systems, and cloud-native architectures. The ideal candidate will lead the development of enterprise-scale AI solutions, mentor engineering teams, define technical roadmaps, and ensure the successful delivery of intelligent, reliable, and scalable AI applications that create measurable business impact.

In this role, you will

AI/ML & NLP Leadership

• Lead the end-to-end development and deployment of Machine Learning and NLP solutions, from problem definition through productionisation.
• Design and implement feature engineering, model training, validation, evaluation, and monitoring pipelines.
• Establish best practices for model lifecycle management, explainability, fairness, and governance.
• Drive continuous improvement of model performance, scalability, and operational efficiency.
• Collaborate with business stakeholders to identify AI-driven opportunities and translate them into practical solutions.

LLM & Generative AI Development

• Design, develop, and deploy enterprise-grade applications powered by Large Language Models (LLMs).
• Build solutions leveraging models such as GPT, Llama, Gemini, Claude, and other leading foundation models.
• Develop and optimize prompt engineering strategies, prompt orchestration, evaluation frameworks, and fine-tuning workflows.
• Integrate LLM capabilities into enterprise applications while optimizing performance, latency, reliability, and cost.
• Evaluate emerging LLM technologies and recommend adoption strategies.

Retrieval-Augmented Generation (RAG) Systems

• Architect and implement scalable RAG pipelines utilizing embeddings, vector databases, and semantic retrieval techniques.
• Design and optimize document ingestion, indexing, retrieval, ranking, and generation workflows.
• Improve retrieval relevance, response quality, and contextual accuracy.
• Work with vector stores and search technologies such as:
• Elasticsearch
• OpenSearch
• FAISS
• Other vector database platforms
• Build hybrid search solutions combining semantic and keyword-based retrieval techniques.

Multi-Agent AI Systems

• Design and develop multi-agent AI architectures to automate complex workflows and decision-making processes.
• Define agent orchestration frameworks, communication patterns, planning strategies, and tool integration mechanisms.
• Build agent collaboration, memory management, reasoning, and execution frameworks.
• Develop reusable agentic AI platforms and accelerators for enterprise-scale adoption.
• Evaluate and implement emerging best practices in autonomous and collaborative AI systems.

System Architecture & Scalability

• Design scalable AI services using microservices, APIs, and cloud-native architectural patterns.
• Build highly available, secure, and observable AI platforms.
• Design solutions supporting real-time, near real-time, and batch inference workloads.
• Optimize infrastructure utilization, cost efficiency, and system performance.
• Implement monitoring, logging, tracing, and operational controls for production AI systems.

Technical Leadership & Mentorship

• Provide technical leadership and architectural guidance to AI engineers, ML engineers, and data scientists.
• Conduct solution reviews, architecture reviews, code reviews, and technical assessments.
• Establish engineering standards, development practices, and AI governance processes.
• Mentor team members and support skill development across AI and ML domains.
• Collaborate closely with product managers, business teams, architects, and engineering leaders to ensure successful delivery.

Required Qualifications

Machine Learning & NLP

• Strong expertise in:
• Natural Language Processing (NLP)
• Tokenization
• Named Entity Recognition (NER)
• Text Classification
• Embeddings
• Transformer-based Architectures
• Strong understanding of traditional machine learning and deep learning techniques.
• Hands-on experience with frameworks such as:
• PyTorch
• TensorFlow
• Equivalent ML frameworks
• Experience developing, evaluating, and deploying production-grade ML models.

LLMs & Generative AI

• Hands-on experience with:
• GPT
• Llama
• Gemini
• Claude
• Other Foundation Models
• Strong expertise in:
• Prompt Engineering
• Fine-Tuning
• Model Evaluation
• AI Application Development
• Experience with Generative AI frameworks such as:
• LangChain
• Google ADK
• Similar AI orchestration frameworks
• Understanding of model selection, benchmarking, guardrails, and response optimization.

Retrieval-Augmented Generation (RAG)

• Expertise in:
• Vector Databases
• Semantic Search
• Embedding Models
• Retrieval Optimization
• Experience with:
• Elasticsearch
• OpenSearch
• FAISS
• Similar retrieval platforms
• Strong understanding of ranking strategies and knowledge retrieval architectures.

Multi-Agent Systems

• Experience building and deploying multi-agent applications and workflows.
• Knowledge of:
• Agent Orchestration Frameworks
• Tool Calling Mechanisms
• Planning Strategies
• Agent Coordination Patterns
• Memory Management Approaches
• Strong understanding of emerging concepts and best practices in Agentic AI systems.

Programming & Systems Engineering

• Strong programming skills in Python.
• Experience designing and developing:
• REST APIs
• gRPC Services
• Distributed Systems
• Experience with:
• Docker
• Kubernetes
• Containerized Deployments
• Understanding of software engineering best practices, design patterns, and scalable application development.

Cloud & Platform Engineering

• Experience with one or more cloud platforms:
• Microsoft Azure
• Amazon Web Services (AWS)
• Google Cloud Platform (GCP)
• OpenShift Container Platform (OCP)
• Experience deploying and managing cloud-native AI applications and services.
• Understanding of scalability, security, reliability, and high-availability patterns.

Desired Qualifications

• Experience with MLOps practices including:
• ML CI/CD Pipelines
• Model Monitoring
• Model Governance
• Drift Detection
• Experience with LLM evaluation and benchmarking frameworks.
• Knowledge of Knowledge Graphs, Graph Databases, and Hybrid Search architectures.
• Contributions to open-source AI, ML, or NLP projects.
• Familiarity with Responsible AI, AI Security, Governance, and Compliance frameworks.
• Experience working with enterprise-scale AI transformation initiatives.
• Excellent leadership, stakeholder management, and cross-functional collaboration skills.
• Strong analytical thinking, problem-solving, and decision-making capabilities.
• Self-driven with a strong sense of ownership and accountability.

Job Expectations

• Lead the successful delivery of enterprise-scale AI and Generative AI solutions.
• Drive innovation through the adoption of modern AI, LLM, RAG, and Agentic AI technologies.
• Provide technical leadership and architectural direction for AI initiatives.
• Ensure AI solutions meet scalability, reliability, security, and performance requirements.
• Establish and promote engineering excellence, development standards, and AI governance practices.
• Mentor and develop high-performing AI engineering teams.
• Collaborate with business, product, data, and engineering stakeholders to deliver measurable business outcomes.
• Stay current with rapidly evolving AI technologies, frameworks, and industry trends.
• Champion a culture of innovation, accountability, continuous learning, and operational excellence.

About NTT DATA

NTT DATA is a $30 billion business and technology services leader, serving 75% of the Fortune Global 100. We are committed to accelerating client success and positively impacting society through responsible innovation. We are one of the world's leading AI and digital infrastructure providers, with unmatched capabilities in enterprise-scale AI, cloud, security, connectivity, data centers and application services. our consulting and Industry solutions help organizations and society move confidently and sustainably into the digital future. As a Global Top Employer, we have experts in more than 50 countries. We also offer clients access to a robust ecosystem of innovation centers as well as established and start-up partners. NTT DATA is a part of NTT Group, which invests over $3 billion each year in R&D.

Whenever possible, we hire locally to NTT DATA offices or client sites. This ensures we can provide timely and effective support tailored to each client’s needs. While many positions offer remote or hybrid work options, these arrangements are subject to change based on client requirements. For employees near an NTT DATA office or client site, in-office attendance may be required for meetings or events, depending on business needs. At NTT DATA, we are committed to staying flexible and meeting the evolving needs of both our clients and employees. NTT DATA recruiters will never ask for payment or banking information and will only use @nttdata.com, @nttdatafed.com and @talent.nttdataservices.com email addresses. If you are requested to provide payment or disclose banking information, please submit a contact us form, 

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