Intermediate Forward Deployed Engineer
About The Position
The Intermediate Forward Deployed Engineer is an experienced technical contributor embedded in cross-functional Forward Deployed Engineering (FDE) PODs working with clients. The role turns business priorities into production-ready cloud and AI solutions, primarily focused on Agentic AI enterprise adoption and selected modernization engagements using AWS and NTT DATA platforms. The engineer provides hands-on technical ownership of defined AI use cases, solution components, and workstreams, from discovery through first agentic production deployment and handover. The role combines software and product engineering, cloud architecture, AI agent orchestration, data integration, and business understanding to deliver solutions within real enterprise constraints. The engineer works with a good degree of autonomy on assigned areas and collaborates with Senior FDEs and other technical contributors on complex or cross-cutting decisions. As a technical bridge between customer needs, business outcomes, and technology solutions, the engineer engages directly with client business and technical stakeholders. They collaborate with the FDE Center of Excellence, hyperscalers, AI Studio, and other NTT DATA product teams to apply platforms, accelerators, and reusable assets, and to capture field learning. The role may also 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 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, including 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 of 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.
- Understanding of evaluation engineering, AgentOps, observability, regression testing, production reliability, and human-in-the-loop controls.
- 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 the ability to explain 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, AWS Certified Cloud Practitioner, AWS Certified Solutions Architect Associate, and AWS Certified AI Practitioner form the expected baseline. AWS Certified Generative AI Developer Professional is the target credential before AWS agentic customer engagement.
Nice To Haves
- Experience supporting opportunity qualification, solution shaping, effort estimation, or proposal development.
- Machine Learning Engineer Associate or Data Engineer Associate certification to strengthen seat-specific readiness.
- Equivalent Azure, Google Cloud, or other platform certifications; these are valued and may be required for those deployment paths.
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 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 with client and NTT DATA engineering teams.
- Apply evaluation and production quality standards, 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, escalating cross-cutting risks when needed.
- Contribute to rapid delivery through frequent working increments, disciplined engineering practices, and timely escalation of access, dependency, and quality risks.
- Support and, when 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 other POD members; share knowledge 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; support proposals, estimates, and solution shaping where appropriate.
- Track technical quality, implementation risk, outcome alignment, and handover readiness for assigned components or workstreams; 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
