Intermediate Forward Deployed Engineer

Description

This intermediate Forward Deployed Engineer role sits in cross-functional FDE pods embedded with clients. The main focus is enterprise adoption of agentic AI, plus selected modernization work, mostly on AWS and the company's own platforms.

You get hands-on ownership of defined AI use cases, solution parts and workstreams, from discovery through the first agentic production deployment and handover. You combine software and product engineering, cloud architecture, agent orchestration, data integration and business understanding to deliver working systems under real enterprise constraints.

You work with a good degree of independence in your areas and bring in Senior FDEs on complex or cross-cutting decisions. You are the link between client needs, business results and technology, speaking directly with client business and technical staff. You do not lead the POD overall.

You may also help with opportunity qualification, solution shaping, and moving teams to operate and scale the solution. You work with the FDE Center of Excellence, hyperscalers, AI Studio and other product teams to apply platforms, accelerators and reusable assets, and to feed field learning back into future delivery.

The company is a $30+ billion business and technology services provider serving 75% of the Fortune Global 100, with experts in more than 70 countries. It is part of the company Group, which invests over $3 billion a year in R&D. The role reports to the FDE Practice Lead.

Responsibilities

• Own defined components or workstreams from problem discovery and design through build, first production release and handover, as an experienced contributor in the POD.
• Turn client priorities into technical requirements, and help define scope, named business outcomes, production access needs and measurable success criteria.
• Design solution components and help shape the wider architecture across data, integration, domain modeling, agent orchestration, evaluation, operations, security and governance.
• Build and connect APIs, data pipelines, MCP servers, ontologies, knowledge bases, agent workflows, reusable skills and operational tooling alongside client and company engineering teams.
• Meet the agreed evaluation and 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 your components, and escalate cross-cutting risks.
• Keep delivery moving with frequent working increments, sound engineering practice, and early escalation of access, dependency and quality risks.
• Run or support technical workshops and working sessions, and explain design choices, tradeoffs, progress and findings to client stakeholders and engineering teams.
• Work closely with Senior FDEs, specialists and POD members, share knowledge, and support less experienced engineers day to day.
• Contribute reusable assets and structured field feedback to the FDE Center of Excellence, product teams and hyperscalers.
• Track quality, implementation risk, outcome alignment and handover readiness for your workstreams, and escalate blockers while driving fixes with the POD or delivery lead.
• Spot adjacent use cases, improvement ideas and product signals in the field, and send them to the right account, product or delivery owner without harming current outcomes.
• Support proposals, estimates and solution shaping where appropriate.

Requirements

• Solid hands-on experience building and deploying cloud, data or AI systems in enterprise settings.
• Strong software or product engineering skills, with real experience in enterprise solution architecture and production delivery.
• Good depth in at least one FDE competency, plus working knowledge of data and integration, domain modeling, agent orchestration, evaluation and governance, applying company and partner platforms, accelerators and reusable assets.
• Practical use of the AWS AI and cloud stack, such as Amazon Bedrock, AgentCore, SageMaker, Neptune, OpenSearch, CloudWatch, IAM, KMS and CloudTrail, or equivalent platform services.
• Experience building or contributing to agentic systems, covering tool schemas, context and memory, model selection, RAG, MCP servers, knowledge bases or 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 architecture, data platforms, data quality and legacy system constraints.
• Ability to build security, responsible AI, sovereignty, compliance and operational controls into design and delivery, with specialist support where needed.
• Clear client communication, with the ability to explain technical decisions and their implications to business and technical audiences.
• Ability to work independently, make pragmatic technical calls, ask for guidance when decisions affect the wider solution, and own your outcomes.
• Working experience with Git, CI/CD, infrastructure as code, containers, automated testing and cloud operations.
• Product mindset: build reusable capability, give structured feedback, drive client adoption and hand over successfully rather than deliver one-off work.
• Degree in computer science, engineering, information technology or a related field, or equivalent practical experience.
• Expected baseline for the AWS path: AWS Certified Cloud Practitioner, AWS Certified Solutions Architect Associate and AWS Certified AI Practitioner.
• Target credential before AWS agentic customer engagement: AWS Certified Generative AI Developer Professional. Machine Learning Engineer Associate or Data Engineer Associate strengthen readiness for specific seats.
• Equivalent Azure, Google Cloud and other platform certifications are valued and may be required for those deployment paths.
• Typically 5 to 8 years of overall experience, including at least 3 years of hands-on cloud, data, software or AI engineering.
• Experience in a large enterprise, product organization or technology services environment.
• Experience contributing to production systems that integrate enterprise data, applications, security controls and cloud services.
• Experience owning components or workstreams through design, build, testing, deployment, production readiness, handover and client team enablement within a wider team.
• Experience running client workshops, presenting technical findings and resolving delivery problems collaboratively.
• Desirable: experience supporting opportunity qualification, solution shaping, effort estimation or proposal development.

Conditions

The starting pay range is 92,000.00 - 131,000.00 - 170,000.00 USD per year, covering minimum and maximum target pay across all US locations.

Actual pay depends on work location, experience, technical skills and other qualifications.

The pay note calls this a remote role, while the workplace type is listed as hybrid working.

The company is committed to diversity and inclusion.