Intermediate Forward Deployed Engineer

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Role: Intermediate Forward Deployed Engineer

The Intermediate Forward Deployed Engineer is an experienced technical contributor within cross functional FDE PODs embedded with clients. The role turns business priorities into production ready cloud and AI solutions, with a primary focus on Agentic AI Enterprise Adoption and selected modernization engagements across primarily AWS and NTT DATA platforms.

Job Description Summary

The primary objective of this role is to provide hands on engineering and technical ownership of defined AI use cases, solution components and workstreams from discovery through first agentic production deployment and handover. The Intermediate Forward Deployed Engineer combines software and product engineering, cloud architecture, AI agent orchestration, data integration and business understanding to deliver working solutions against real enterprise constraints. They operate with a good degree of autonomy on assigned areas, while working with Senior FDEs and other technical contributors on complex or cross cutting decisions.

The Intermediate Forward Deployed Engineer serves as a technical bridge between customer needs, business outcomes and technology solutions. They engage directly with client business and technical stakeholders and contribute as an experienced member of the FDE POD, taking ownership of defined technical workstreams without carrying overall POD leadership. They work with the FDE Center of Excellence, hyperscalers, AI Studio and other NTT DATA product teams to apply company and partner platforms, accelerators and reusable assets, and to capture field learning that can improve future delivery. They may also support opportunity qualification, solution shaping and transition to operate and scale teams.

The Intermediate Forward Deployed Engineer is expected to have solid hands on experience building and deploying cloud, data or AI systems in enterprise environments. The profile combines strong engineering capability with practical solution architecture, client communication and the judgement required to work effectively in embedded delivery teams.

Job Description

Key Roles and Responsibilities

• Act as an experienced technical contributor within an FDE POD, taking ownership of defined solution components or technical workstreams from problem discovery and design through implementation, first production deployment and handover.
• Translate client priorities into clear technical requirements and contribute to engagement scope, named business outcomes, production access needs and measurable success criteria.
• Design solution components and contribute to the wider solution architecture across data, integration, domain modeling, agent orchestration, evaluation, operations, security and governance.
• Build and integrate APIs, data pipelines, MCP servers, ontologies, knowledge bases, agent workflows, reusable skills and operational tooling, working directly with client and NTT DATA engineering teams.
• Apply the agreed evaluation and production quality bar, including real case testing, regression gates, observability, human approval points and operational readiness.
• Apply security, responsible AI, identity, audit, data residency, sovereignty and regulatory requirements within assigned solution components and escalate cross cutting risks when required.
• Contribute to a rapid delivery cadence through frequent working increments, disciplined engineering practices and timely escalation of access, dependency and quality risks.
• Support and, where appropriate, facilitate technical workshops and working sessions, and communicate solution design, tradeoffs, delivery progress and technical findings to client stakeholders and engineering teams.
• Collaborate closely with Senior FDEs, specialist contributors and other POD members, contribute knowledge to the team and support less experienced engineers through day to day technical collaboration.
• Contribute reusable assets and structured field feedback for the FDE Center of Excellence, product teams and hyperscalers, and support proposals, estimates and solution shaping where appropriate.
• Track technical quality, implementation risk, outcome alignment and handover readiness for assigned components or workstreams, escalating blockers and contributing to corrective action with the POD or delivery lead.
• Identify adjacent use cases, improvement opportunities and product signals observed in the field and route them to the appropriate account, product or delivery owners without compromising current engagement outcomes.

Knowledge, Skills and Attributes

• Strong hands on software or product engineering capability, with practical experience of enterprise solution architecture and production delivery.
• Good depth in at least one FDE competency and working knowledge across data and integration, domain modeling, agent orchestration, evaluation and governance, with the ability to apply company and partner platforms, products, accelerators and reusable assets.
• Practical knowledge of the AWS AI and cloud stack, including Amazon Bedrock, AgentCore, SageMaker, Neptune, OpenSearch, CloudWatch, IAM, KMS and CloudTrail, or equivalent platform services.
• Experience building or contributing to agentic systems using tool schemas, context and memory, model selection, RAG, MCP servers, knowledge bases or knowledge graphs, and agent integrations.
• Good understanding of evaluation engineering, AgentOps, observability, regression testing, production reliability and human in the loop controls.
• Good knowledge of enterprise integration, APIs, event driven architectures, data platforms, data quality and common legacy system constraints.
• Ability to apply security, responsible AI, sovereignty, compliance and operational control requirements within solution design and delivery, with support from specialists where needed.
• Strong client engagement and communication skills, with the ability to explain technical decisions and implications clearly to both business and technical stakeholders.
• Ability to operate independently on defined areas of work, make pragmatic technical decisions, ask for guidance when decisions affect the wider solution and remain accountable for assigned outcomes.
• Experience with modern engineering practices, including Git, CI/CD, infrastructure as code, containers, automated testing and cloud operations.
• Product mindset focused on reusable capability, structured feedback, client adoption and successful transfer rather than one off delivery.

Academic Qualifications and Certifications

• Degree in computer science, engineering, information technology or a related field, or equivalent practical experience.
• AWS Certified Cloud Practitioner, AWS Certified Solutions Architect Associate and AWS Certified AI Practitioner form the expected baseline for the AWS deployment path. AWS Certified Generative AI Developer Professional is the target credential before AWS agentic customer engagement, with Machine Learning Engineer Associate or Data Engineer Associate strengthening seat specific readiness. Equivalent Azure, Google Cloud and other platform certifications are valued and may be required for those deployment paths.

Required Experience

• Demonstrable experience in software, data, cloud, platform or AI engineering roles within a large enterprise, product organization or technology services environment.
• Typically 5 to 8 years of overall experience, including at least 3 years of hands on cloud, data, software or AI engineering experience.
• Experience contributing to production systems that integrate enterprise data, applications, security controls and cloud services.
• Experience owning defined technical components or workstreams across design, build, testing and deployment, contributing to production readiness, handover and client team enablement as part of a wider POD or engagement team.
• Experience participating in client workshops, presenting technical findings and resolving delivery challenges in a collaborative manner.

Desirable Experience

• Experience supporting opportunity qualification, solution shaping, effort estimation or proposal development is desirable.

Reporting To: FDE Practice Lead

NTT DATA provides a reasonable range of compensation for U.S.-based positions. The starting pay range for this remote role is 92,000.00 - 131,000.00 - 170,000.00 USD Annual. This range reflects the minimum and maximum target compensation for the position across all US locations. Actual compensation will depend on a number of factors, including the candidate’s actual work location, relevant experience, technical skills, and other qualifications.

Workplace type

Hybrid Working

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 70 countries. We also offer clients access to a robust ecosystem of innovation centers as well as

established and start-up partners. NTT DATA is part of NTT Group, which invests over $3 billion each

year in R&D.

Equal Opportunity Employer
NTT DATA is proud to be an Equal Opportunity Employer with a global culture that embraces diversity. We are committed to providing an environment free of unfair discrimination and harassment. We do not discriminate based on age, race, colour, gender, sexual orientation, religion, nationality, disability, pregnancy, marital status, veteran status, or any other protected category. Join our growing global team and accelerate your career with us. Apply today.

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