New opportunity

Forward Deployed Engg. - Lead

NTTD Info Pro Ser Pte Ltd · Bangalore, India

About this role

We are seeking a customer-focused Forward Deployed Engineer (FDE) to help enterprises identify, validate, design, and scale AI-powered solutions that deliver measurable business outcomes. Operating at the intersection of AI consulting, solution architecture, rapid prototyping, and delivery advisory, you will work closely with business stakeholders, enterprise architects, platform teams, and engineering squads to transform opportunities into scalable AI solutions while balancing business value, technical feasibility, governance, and production readiness.

Responsibilities

Lead the design of enterprise grade AI solutions and guide customers through architecture decisions aligned with business and technology strategies, focusing on RAG, agentic AI, multi-agent systems, and conversational AI.
Define architecture blueprints, evaluate trade-offs, and establish scalable implementation approaches.
Conduct discovery workshops, evaluate existing technology landscapes, and build prototypes, Proofs of Concept (POCs), and Proofs of Value (POVs).
Define implementation roadmaps and partner with engineering teams to industrialize approved solutions.
Define AI governance approaches, guardrails, and embed Responsible AI principles regarding security, compliance, privacy, and data governance.
Assess production readiness, including AIOps, observability, reliability engineering, and cost optimization.
Provide multi-cloud and platform-neutral guidance across major hyperscalers like Azure, AWS, and GCP.
Act as a trusted advisor, facilitating workshops, developing business cases, and presenting recommendations to executive stakeholders.

Required qualifications

8+ years of experience in Solution Architecture, Technology Consulting, AI Engineering, Customer Engineering, or related disciplines.
Experience in designing and delivering enterprise technology solutions.
Strong understanding of Generative AI, Agentic AI, and modern AI architectures.
Experience with RAG, agents, multi-agent systems, and enterprise integration patterns.
Familiarity with AI governance, security, compliance, and Responsible AI principles.
Strong communication, facilitation, and stakeholder management skills with experience working directly with enterprise customers and senior stakeholders.

Preferred qualifications

Experience with agent development and orchestration frameworks including LangChain, LangGraph, Semantic Kernel, LlamaIndex, AutoGen, and CrewAI.
Experience with Azure AI Services/Foundry, AWS Bedrock, Vertex AI, Databricks, Snowflake, Copilot Studio, and Power Platform.
Familiarity with enterprise search, knowledge management platforms, and ProCode/NoCode development approaches.

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

Skills for this role

Retrieval-Augmented Generation (RAG)Agentic AIMulti-agent systemsConversational AIIntelligent automationGenerative AILangChainLangGraphSemantic KernelLlamaIndexAutoGenCrewAIMicrosoft AzureAmazon Web Services (AWS)Google Cloud Platform (GCP)DatabricksSnowflakeCopilot StudioPower Platform

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

Enterprise AI Solution Architecture & Design
Lead the design of enterprise-grade AI architectures, focusing on Retrieval-Augmented Generation (RAG), agentic AI, multi-agent systems, and conversational platforms, ensuring they align with client business strategies and integration patterns.
Rapid Prototyping & Value Validation
Conduct discovery workshops, build early prototypes, and deliver Proofs of Concept (POCs) and Proofs of Value (POVs) to quickly validate technical feasibility and business outcomes before major delivery investments occur.
AI Governance & Responsible AI Integration
Embed Responsible AI principles, safety guardrails, security requirements, compliance obligations, and data governance policies directly into solution designs for enterprise deployments.
Stakeholder Engagement & Trusted Advisory
Act as a trusted advisor to enterprise customers and executive stakeholders by facilitating discovery workshops, assessing current-state technology ecosystems, and shaping transformation roadmaps.
Production Readiness & Scalability Oversight
Ensure solutions operate reliably at enterprise scale by assessing production hardening, AIOps (MLOps, LLMOps, AgentOps), observability, reliability engineering, and cost optimization.

Likely challenges to think through

Bridging Business Value and Technical Feasibility in Prototypes
The challenge lies in rapidly building POCs that look promising in a sandbox while ensuring they can scale sustainably within rigid enterprise constraints. Prepare by practicing structured tradeoff analyses between speed of delivery and long-term production readiness.
Navigating Complex Multi-Cloud and Platform Constraints
Balancing platform-neutral architectural guidance while maintaining deep expertise across major hyperscalers like Azure, AWS, and GCP can lead to fragmentation. Prepare by reviewing multi-cloud patterns and standardized AI platform services across major cloud providers.
Enforcing Rigorous AI Governance Without Stifling Innovation
Enterprises demand strict adherence to privacy, compliance, and Responsible AI guardrails, which can slow down rapid prototyping cycles. Prepare by developing frameworks that embed security and compliance reviews early in the design phase.

Practice your answers

Technical practice

Designing Enterprise Retrieval-Augmented Generation (RAG) Architectures

What to coverAs a suggested practice topic, be prepared to discuss how you design low-latency, high-accuracy RAG pipelines for enterprise knowledge platforms. Address trade-offs between chunking strategies, vector database selection, hybrid search, and reranking mechanisms to mitigate hallucination and context window limits.

Multi-Agent Orchestration and Autonomous Workflow Patterns

What to coverPractice explaining how you architect multi-agent systems using frameworks like LangGraph, AutoGen, or CrewAI. Discuss patterns for task decomposition, inter-agent communication, state management, and handling failure recovery when autonomous agents interact with enterprise APIs.

Balancing Model Portability and Hyperscaler AI Services

What to coverConsider how you provide platform-neutral guidance while leveraging native services across Azure, AWS, and GCP (such as Azure AI Foundry, AWS Bedrock, or Vertex AI). Discuss the architectural trade-offs of tightly coupling solutions to proprietary cloud services versus maintaining multi-cloud portability.

AI Governance, Guardrails, and Responsible AI Implementation

What to coverPrepare to explain how you embed Responsible AI principles, security requirements, data privacy standards, and compliance guardrails directly into enterprise AI architecture reviews. Focus on techniques for mitigating bias, enforcing data governance, and managing AI risk.

Production Hardening, Observability, and LLMOps

What to coverReview how you approach production readiness for generative AI solutions, including monitoring latency, tracking token costs, implementing observability frameworks, and managing LLMOps/AgentOps lifecycles to ensure enterprise reliability.

Rapid Prototyping and Proof-of-Value (POV) Methodology

What to coverBe ready to discuss your approach to conducting discovery workshops, assessing client technology landscapes, and rapidly building Proofs of Concept (POCs) to validate business value before committing to large-scale engineering investments.

Enterprise Integration Patterns for AI Capabilities

What to coverPractice explaining how you integrate generative AI models and knowledge platforms with existing enterprise systems, legacy databases, and workflow automation tools while maintaining security and data integrity.

Behavioural practice

Navigating Conflicting Priorities Between Business Value and Technical Feasibility

What to coverSuggest a situation where you worked with enterprise stakeholders who wanted an aggressive AI feature that exceeded technical feasibility or governance standards. Describe the action you took to facilitate alignment, adjust expectations, and define a viable roadmap, and detail the resulting business outcome.

Managing Senior Stakeholders and Building Trusted Advisory Relationships

What to coverDescribe a scenario where you engaged with executive leadership or enterprise architects who were skeptical about adopting new AI architectures. Explain how you built technical credibility, addressed their concerns through discovery workshops or prototypes, and secured buy-in.

Guiding a Solution from Rapid Prototyping to Enterprise Production

What to coverProvide an example of a time you transitioned an AI Proof of Concept (POC) into a production-grade enterprise solution. Detail how you addressed production hardening, scalability planning, cost optimization, and operational handoff to engineering squads.

Enforcing AI Governance and Security Constraints Under Tight Deadlines

What to coverRecall a situation where strict compliance, data privacy, or Responsible AI guardrails conflicted with a client's desire for rapid deployment. Explain how you navigated the governance review process without stalling momentum or compromising security standards.

Sharpen your knowledge

Topics to prepare

Enterprise AI Solution Architecture & Reference Patterns
Exercise: Design a reference architecture for an enterprise knowledge platform incorporating RAG and multi-agent systems, detailing integration patterns with existing data stores. Relevance: Directly matches the requirement to lead enterprise-grade AI solution design and establish scalable implementation approaches.
AI Governance, Security, and Responsible AI Principles
Exercise: Create a governance checklist covering data privacy, security compliance, and risk management guardrails for deploying generative AI models in corporate environments. Relevance: Aligns with the core responsibility of ensuring solutions adhere to compliance obligations and Responsible AI principles.
Multi-Cloud Platform Strategy & Hyperscaler AI Services
Exercise: Compare the native AI and data services of Azure, AWS, and GCP (such as Azure AI Foundry, AWS Bedrock, and Vertex AI) for deploying portable LLM solutions. Relevance: Addresses the expectation for deep expertise in at least one hyperscaler and platform-neutral guidance.
Production Readiness, AIOps, and Cost Optimization
Exercise: Outline a framework for monitoring LLMOps/AgentOps performance, evaluating latency, tracking token costs, and handling production hardening. Relevance: Prepares you for assessing whether solutions can successfully and reliably operate at enterprise scale.

Skills to revise

Agentic AI & Multi-Agent Orchestration Frameworks
Exercise: Build a prototype using orchestration frameworks like LangChain, LangGraph, or AutoGen to automate a multi-step enterprise workflow. Relevance: Fulfills the preferred qualification for experience with agent development and orchestration tools.
Rapid Prototyping & Proof-of-Value Execution
Exercise: Rapidly stand up a Proof of Concept (POC) combining a knowledge base with a retrieval-augmented generation pipeline to validate a specific business use case. Relevance: Directly ties to the core duty of conducting discovery workshops and validating business value quickly through prototypes.
Enterprise Stakeholder Advisory & Workshop Facilitation
Exercise: Draft a structured agenda and discovery questionnaire for a client technical workshop aimed at prioritizing AI opportunities and building business cases. Relevance: Aligns with the requirement for strong communication, facilitation, and client advisory skills.

Your preparation checklist

Draft an Enterprise AI Solution Architecture Blueprint
Create a reference architecture diagram and accompanying document outlining a Retrieval-Augmented Generation (RAG) and multi-agent system, detailing how it integrates with enterprise data platforms across major cloud providers like Azure, AWS, or GCP.
Build a Multi-Agent Prototype with Orchestration Frameworks
Develop a working proof-of-concept (POC) using tools like LangChain, LangGraph, or AutoGen that demonstrates autonomous task execution, inter-agent communication, and error handling for an enterprise workflow.
Prepare an AI Governance and Responsible AI Compliance Checklist
Compile a structured framework addressing security requirements, data privacy standards, compliance obligations, and bias mitigation guardrails to demonstrate how you ensure production readiness and risk management.
Structure a Client Discovery Workshop Agenda
Draft a facilitator guide and questionnaire designed for enterprise stakeholders and executive leaders to assess current-state technology landscapes, prioritize AI use cases, and build business value cases.
Formulate Targeted Questions for the Employer
Prepare thoughtful questions regarding NTT DATA's enterprise client engagement models, multi-cloud platform strategy, and how engineering squads transition prototypes into production-scale deployments.

Availability last checked: 4 Nov 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy

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