Intermediate Forward Deployed Engineer
About The Position
The Intermediate Forward Deployed Engineer is an experienced technical contributor embedded in cross-functional Forward Deployed Engineer (FDE) PODs with clients. The role translates business priorities into production-ready cloud and AI solutions, primarily focused on Agentic AI Enterprise Adoption and selected modernization engagements across AWS and NTT DATA platforms. The engineer provides hands-on ownership of defined AI use cases, solution components, and workstreams from discovery through first agentic production deployment and handover. The work combines software and product engineering, cloud architecture, AI agent orchestration, data integration, and business understanding to deliver solutions within real enterprise constraints. The role operates with autonomy over assigned areas and collaborates with Senior FDEs and other technical contributors on complex or cross-cutting decisions. The position bridges customer needs, business outcomes, and technology solutions. It involves direct engagement with client business and technical stakeholders, ownership of defined technical workstreams without overall POD leadership, and collaboration with the FDE Center of Excellence, hyperscalers, AI Studio, and other NTT DATA product teams. The engineer applies platforms, accelerators, and reusable assets, captures field learning, and may support opportunity qualification, solution shaping, and transition to teams responsible for operating and scaling solutions.
Requirements
- Demonstrable experience in software, data, cloud, platform, or AI engineering roles in 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, and contributing to production readiness, handover, and client team enablement within a wider POD or engagement team.
- Experience participating in client workshops, presenting technical findings, and resolving delivery challenges collaboratively.
- Strong hands-on software or product engineering capability and practical experience with 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.
- 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, or 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 in solution design and delivery, with specialist support where needed.
- Strong client engagement and communication skills, including explaining technical decisions and implications to business and technical stakeholders.
- Ability to work independently on defined areas, make pragmatic technical decisions, seek 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.
- Degree in computer science, engineering, information technology, or a related field, or equivalent practical experience.
- For the AWS deployment path, the expected baseline credentials are AWS Certified Cloud Practitioner, AWS Certified Solutions Architect Associate, and AWS Certified AI Practitioner.
- AWS Certified Generative AI Developer Professional is the target credential before AWS agentic customer engagement; Machine Learning Engineer Associate or Data Engineer Associate strengthens seat-specific readiness.
- Equivalent Azure, Google Cloud, and other platform certifications are valued and may be required for those deployment paths.
Nice To Haves
- Experience supporting opportunity qualification, solution shaping, effort estimation, or proposal development.
Responsibilities
- Own defined solution components or technical workstreams within an FDE POD, from discovery and design through implementation, first production deployment, and handover.
- Translate client priorities into technical requirements and contribute to engagement scope, business outcomes, production access needs, and measurable success criteria.
- Design solution components and contribute to architecture spanning 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 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 to assigned solution components; escalate cross-cutting risks as needed.
- Contribute to rapid delivery 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; communicate solution design, tradeoffs, progress, and technical findings to client stakeholders and engineering teams.
- Collaborate with Senior FDEs, specialist contributors, and POD members; share knowledge and support less experienced engineers through day-to-day technical collaboration.
- Contribute reusable assets and structured field feedback to the FDE Center of Excellence, product teams, and hyperscalers; support proposals, estimates, and solution shaping where appropriate.
- Track technical quality, implementation risk, outcome alignment, and handover readiness for assigned work; escalate blockers and contribute to corrective action.
- Identify adjacent use cases, improvement opportunities, and product signals, and route them to appropriate account, product, or delivery owners without compromising current engagement outcomes.
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What This Job Offers
Job Type
Full-time
Career Level
Mid Level
