New opportunity

Forward Deployed Engineer

NTT Data · 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 stakeholders to transform opportunities into scalable AI solutions.

Responsibilities

Lead the design of enterprise grade AI solutions including Retrieval-Augmented Generation (RAG), Agentic AI, multi-agent systems, conversational AI, and intelligent automation.
Conduct discovery workshops, evaluate existing technology landscapes, and build prototypes, Proofs of Concept (POCs), and Proofs of Value (POVs).
Establish AI governance approaches, guardrails, and embed Responsible AI principles covering security, compliance, privacy, and data governance.
Assess production readiness, support production hardening, AIOps (MLOps, LLMOps, AgentOps), observability, reliability, and cost optimization.
Provide platform-neutral and polycloud guidance across Microsoft Azure, AWS, and GCP.
Act as a trusted technical advisor, facilitating business and technology discovery workshops and creating transformation roadmaps.

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.
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.
Familiarity with Azure AI Services/Foundry, AWS Bedrock, Vertex AI, Databricks, Snowflake, Copilot Studio, and Power Platform.
Background in enterprise search, knowledge management platforms, and ProCode/NoCode development approaches.

Benefits

Equal Opportunity Employer with a global culture embracing diversity and inclusion.

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

Skills for this role

Generative AIAgentic AIRAGMulti-agent systemsConversational AIIntelligent automationAI GovernanceResponsible AIAIOpsMLOpsLLMOpsAgentOpsLangChainLangGraphSemantic KernelLlamaIndexAutoGenCrewAIAzure AI ServicesAWS BedrockVertex AIDatabricksSnowflakeCopilot 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
Lead the design of enterprise-grade AI systems, focusing on Retrieval-Augmented Generation (RAG), agentic AI, multi-agent frameworks, and conversational platforms, ensuring they align with overarching business and technology strategies.
Rapid Prototyping and Proof of Value
Conduct discovery workshops and build rapid prototypes, Proofs of Concept (POCs), and Proofs of Value (POVs) using enterprise platforms to validate business value and technical feasibility before delivery investment.
AI Governance and Responsible AI Integration
Establish governance approaches, guardrails, and Responsible AI principles aligned with security, compliance, privacy, data governance, and risk management practices within enterprise environments.
Production Readiness and Scalability
Assess production readiness, support production hardening, implementation of AIOps (MLOps, LLMOps, AgentOps), observability, reliability, and cost optimization for solutions operating at enterprise scale.
Client Advisory and Stakeholder Management
Act as a trusted technical advisor throughout the customer AI journey, facilitating cross-functional discussions among business leaders, enterprise architects, platform teams, and engineering leadership.

Likely challenges to think through

Balancing Speed of Prototyping with Production Scalability
The challenge lies in rapidly building convincing prototypes or POVs to validate business value while simultaneously establishing robust architectural foundations, multi-cloud portability, and AIOps practices to prevent technical debt during industrialization. Prepare by structuring modular prototype designs that cleanly separate experimental orchestration layers from production-grade security, data governance, and monitoring pipelines.
Navigating Complex Enterprise Governance and Compliance
Integrating generative and agentic AI into legacy enterprise environments requires strict adherence to security, data privacy, and compliance frameworks without stifling innovation. Prepare by studying enterprise risk management frameworks for AI, data governance policies, and guardrails implementation patterns across major hyperscalers.
Bridging Business Stakeholders and Technical Engineering Teams
Operating at the intersection of AI consulting and technical architecture demands translating high-level business objectives into concrete architectural blueprints while managing differing priorities across executive leadership and engineering squads. Prepare by practicing structured discovery techniques, value realization mapping, and crisp technical communication tailored to both business and technical audiences.

Practice your answers

Technical practice

Designing Enterprise RAG Architectures

What to coverDiscuss how you would design a retrieval-augmented generation pipeline for an enterprise knowledge management platform. Address data ingestion, chunking strategies, vector database selection, hybrid search, and retrieval reranking to ensure low latency and high relevance.

Agentic AI and Multi-Agent Orchestration

What to coverExplain the architectural patterns for building multi-agent systems using frameworks like LangGraph, AutoGen, or CrewAI. Discuss agent communication protocols, state management, loop prevention, and tool-use reliability in production workflows.

AI Governance and Responsible AI Guardrails

What to coverDescribe how you would establish AI governance frameworks to address security, privacy, compliance, and risk management. Focus on techniques for preventing data leakage, enforcing content guardrails, and managing bias in enterprise AI solutions.

Production Hardening and AIOps (MLOps/LLMOps)

What to coverOutline your approach to assessing production readiness and managing AIOps for large language model applications. Cover evaluation frameworks, continuous monitoring, observability, drift detection, and automated fallback mechanisms.

Polycloud Strategy and Platform Selection

What to coverEvaluate the trade-offs of deploying generative AI workloads across Microsoft Azure, AWS, and GCP. Discuss how to provide platform-neutral guidance while leveraging native cloud services such as Azure AI Foundry, Bedrock, or Vertex AI.

Rapid Prototyping and Proof of Value (POV) Design

What to coverExplain how you conduct discovery workshops and evaluate existing enterprise technology landscapes to build rapid prototypes. Discuss how you validate technical feasibility and business value before large-scale delivery investment.

Enterprise Integration and API Patterns

What to coverDetail how you integrate custom AI solutions with existing enterprise backend systems and legacy data stores. Address security boundaries, authentication, rate limiting, and asynchronous messaging patterns for scalable automation.

Cost Optimization and Scalability Planning

What to coverDiscuss strategies for managing and optimizing inference costs for enterprise-grade AI models at scale. Cover model right-sizing, prompt caching, token budgeting, batching strategies, and auto-scaling policies.

Behavioural practice

Managing Conflicting Stakeholder Priorities

What to coverDescribe a situation where business leaders wanted rapid delivery of an AI prototype while enterprise architects demanded strict governance and security compliance. Explain how you facilitated alignment using the Situation-Action-Result format.

Advising Enterprise Customers as a Trusted Technical Advisor

What to coverProvide an example of an engagement where you worked directly with senior stakeholders to shape an AI adoption roadmap. Detail how you handled pushback, managed expectations, and balanced technical feasibility with business value.

Challenging Architectural Decisions and Pivot Management

What to coverShare an experience where you had to challenge an existing customer technology landscape or implementation approach during a discovery workshop. Discuss how you communicated your rationale and guided the team toward a scalable alternative.

Navigating Ambiguity in Early-Stage AI Explorations

What to coverRecount a project where requirements were initially vague or undefined during the discovery phase. Explain how you scoped Proofs of Concept (POCs), defined measurable outcomes, and drove the initiative to successful enterprise adoption.

Sharpen your knowledge

Topics to prepare

Enterprise AI Solution Architecture
Understand how to design scalable architectures covering Retrieval-Augmented Generation, Agentic AI, and multi-agent systems. Relevant for leading architecture decisions, evaluating technical trade-offs, and guiding enterprise customers through solution design.
AI Governance and Responsible AI Principles
Study frameworks for security, compliance, privacy, data governance, and risk management in AI deployments. Relevant for embedding guardrails and ensuring adherence to enterprise compliance standards.
Production Readiness and AIOps
Examine operational considerations including production hardening, ML/LLM/AgentOps, observability, reliability, and cost optimization. Relevant for assessing whether prototypes can scale reliably to enterprise production environments.

Skills to revise

Rapid Prototyping and Solution Validation
Practice building quick prototypes, Proofs of Concept (POCs), and Proofs of Value (POVs) to validate technical feasibility and business value. Relevant for assessing customer technology landscapes and proving out use cases before delivery investment.
Technical Consulting and Stakeholder Facilitation
Develop communication and workshop facilitation skills for engaging directly with enterprise customers and senior leadership. Relevant for conducting discovery sessions, assessing current-state ecosystems, and presenting transformation roadmaps.
Polycloud and AI Platform Strategy Guidance
Familiarize yourself with platform selection across major hyperscalers like Microsoft Azure, AWS, and GCP as well as enterprise data platforms. Relevant for providing platform-neutral guidance and supporting multi-cloud AI strategies.

Your preparation checklist

Prepare Enterprise AI Solution Architecture Walkthroughs
Assemble design artifacts and architectural blueprints demonstrating end-to-end delivery of Retrieval-Augmented Generation (RAG), agentic workflows, and multi-agent systems. Completion looks like having clear case studies ready to present to enterprise stakeholders.
Build a Rapid Prototype and Proof of Concept (POC)
Develop a working prototype utilizing agent orchestration frameworks or hyperscaler AI services (such as Azure AI Foundry, AWS Bedrock, or Vertex AI). Completion looks like a functional demo validating technical feasibility and business value for an enterprise use case.
Document AI Governance and Responsible AI Guardrails
Draft a comprehensive framework covering security, compliance, data privacy, and risk management policies for generative AI deployments. Completion looks like a structured reference document for establishing guardrails in production.
Map Production Readiness and AIOps Strategies
Prepare notes and metrics addressing LLMOps, AgentOps, observability, evaluation frameworks, reliability engineering, and cost optimization. Completion looks like an operational playbook for scaling prototypes into enterprise production environments.
Formulate Polycloud and Platform Strategy Talking Points
Review multi-cloud capabilities and architectural trade-offs across Microsoft Azure, AWS, and GCP. Completion looks like a structured comparison that enables you to provide platform-neutral guidance to enterprise clients.
Prepare Client Advisory and Workshop Facilitation Examples
Summarize past experiences conducting discovery workshops, assessing technical landscapes, and creating transformation roadmaps with senior stakeholders. Completion looks like ready STAR-format stories highlighting stakeholder management and communication skills.
Develop Thoughtful Questions for the Employer
Draft targeted questions regarding NTT Data's enterprise client engagement models, preferred AI orchestration tech stacks, and governance review processes. Completion looks like a curated list of questions to ask interviewers about their delivery approach.

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