Capgemini Financial Services Forward Deployed Engineering Team The FS Applied AI team provides our forward deployed engineering capability as part of the new FS AI Enterprise Hub, Capgemini Financial Services' capability for accelerating AI-led business transformation for our clients. The Hub combines strategy, AI assets, delivery methodologies, partner ecosystems, domain knowledge, and transformation expertise to help clients realise measurable business outcomes through AI. Our forward deployed engineering capability will sit at the centre of this mission, acting as the bridge between client strategy and execution across deal shaping, delivery, and industrialisation. Working alongside leading AI partners and Financial Services experts, our engineers help build repeatable capabilities, AI-enabled products, and transformation approaches that scale across our clients and the wider Capgemini ecosystem.
Our FS AI Enterprise Hub will support our clients across a wide range of areas from Banking and Capital Markets and Insurance Business transformation, to Digital Customer Experience and across all areas of technology transformation.
Job Description Our Forward Deployed Engineers (internally referred to as Outcome Delivery Engineers) are business-focused technologists who help Financial Services clients translate strategic intent into AI-enabled products, workflows, and operating capabilities. They remain accountable from opportunity discovery through delivery, adoption, and value realisation. They are hybrid business technologists, with some bringing stronger domain and business expertise and others offering deeper technical capabilities.
Embedded alongside clients' business and technology teams, our FDEs discover and frame the real problem, define the right solution, and maintain accountability throughout the build and value realisation process. An FDE independently owns a customer use case from discovery through production deployment and handoff, coordinating the contributions of the broader delivery team. They partner with customer stakeholders, product teams, and engineering teams to define the problem, design the solution, build production-quality software, and drive measurable adoption.
FDEs combine strong technical execution with business and domain understanding. Clients rely on them for both engineering judgment and business-value insight, and they shape, build, and industrialise solutions that deliver measurable outcomes. Working closely with our wider business and technology teams, FDEs architect, design, and deliver solutions that realise measurable outcomes for our clients.
Discover and Frame - Lead discovery and problem framing with client stakeholders; separate the real problem from the stated ask.
- Define the success measures, value hypothesis, and target outcomes before anything is built.
- Shape the solution direction and make the key decisions that follow: capability changes, workflow redesign, platform choices, architecture direction, and where AI belongs in the operating model.
Solution and Build - Own the solution architecture and build the critical path hands-on; set the engineering, business, and delivery standard for the workstream.
- Deliver rapid prototypes, proofs of value, and production-ready increments that prove value early and de-risk decisions.
- Steer AI tooling and teams to produce production-quality output, and verify it before it ships.
- Verify generated output against both engineering and business failure classes, including reliability, security, performance, compliance, regulatory requirements, operational feasibility, and business outcomes.
- Bring applied AI and generative AI to the solution as a usable product, not a demo, designing and implementing AI-powered workflows with appropriate human oversight.
- Own the specification and acceptance criteria the pod and its tooling build against and sign off that what shipped is what was specified.
- Drive production readiness- reliability, security, compliance, observability, supportability, and operational sustainability- designed in from the start.
- Design solutions that balance technical excellence, business value, user adoption, and regulatory obligations.
- Bring expertise in one or more areas such as AI and agentic systems, software engineering, data and platform engineering, business architecture, operating-model transformation, workflow redesign, or digital product delivery.
Industrialise and Hand-Off - Turn prototypes into repeatable, reusable assets, playbooks, patterns, and accelerators, and hand off cleanly to delivery or managed services.
- Share client learning across the wider FDE capability.
- Ensure solutions are documented, supportable, measurable, and ready to scale beyond the initial deployment.
Enable and Own - Own the outcome through adoption; lead client demonstrations and pilots, and drive usage with real users.
- Orchestrate SMEs and secure the right depth at the right moment; support transition into delivery and managed services.
- Advise client managers and directors as a trusted partner across business and technology domains.
- Design and enable the operating-model changes the solution requires, including process redesign, capability mapping, workflow transformation, organisation impact, and technology adoption.
- Design the adoption and change approach, including stakeholder engagement, training, communications, readiness, and benefit tracking.
- Own the value hypothesis and track benefit realisation after go-live.
- Make the solution supportable by whoever inherits it through appropriate documentation, observability, runbooks, governance, and operational processes.
Shape and Sell - Contribute to pursuits and pre-sales: solution shaping, proof-of-value, and credible business and technical storytelling.
- Support opportunity qualification, client workshops, value articulation, and solution demonstrations.
Required Qualifications - Professional experience in software engineering, solution architecture, technical consulting, business consulting, product management, or a comparable customer-facing role.
- Demonstrated ability to independently lead customer workstreams from discovery through deployment and adoption.
- Ability to independently navigate ambiguous requirements and convert them into measurable outcomes and executable delivery plans.
- Strong customer-facing communication, facilitation, stakeholder-management, and workshop leadership skills.
- Ability to explain technical concepts and business trade-offs to both engineering and business audiences.
- Working knowledge of security, privacy, compliance, governance, and software lifecycle practices.
- Working domain knowledge of financial services in at least one area of banking or insurance, including key products, business processes, customer journeys, data domains, controls, and regulatory requirements.
- Ability to map business processes, data, controls, and regulatory obligations to solution designs and measurable outcomes.
- Hands-on experience applying AI, generative AI, workflow automation, analytics, or agentic AI approaches to business problems.
- Familiarity with AI concepts including prompt engineering, retrieval-augmented generation (RAG), tool/function calling, agent orchestration frameworks, evaluation methods, guardrails, and responsible AI principles.
- Experience leading a multidisciplinary team, workstream, or delivery pod.
Preferred Qualifications - Experience delivering AI, generative AI, machine-learning, analytics, or workflow-automation solutions in production environments.
- Experience with cloud-native architectures, enterprise integration patterns, containers, infrastructure as code, or DevOps practices.
- Experience designing and integrating APIs, enterprise systems, data platforms, cloud services, or business-process transformation initiatives.
- Experience with business architecture, operating-model design, capability mapping, value-stream design, or process transformation.
- Domain experience in banking, insurance, customer service, operations, or contact-centre environments.
- Familiarity with regulated environments, responsible AI, model-risk management, data governance, and compliance frameworks.
- Experience supporting pre-sales, solution shaping, business case development, or customer pursuits.
The base compensation range for this role in the posted location is: $130,000-$180,000
Capgemini provides compensation range information in accordance with applicable national, state, provincial, and local pay transparency laws. The base compensation range listed for this position reflects the minimum and maximum target compensation Capgemini, in good faith, believes it may pay for the role at the time of this posting. This range may be subject to change as permitted by law.
The actual compensation offered to any candidate may fall outside of the posted range and will be determined based on multiple factors legally permitted in the applicable jurisdiction.
These may include, but are not limited to: Geographic location, Education and qualifications, Certifications and licenses, Relevant experience and skills, Seniority and performance, Market and business consideration, Internal pay equity.
It is not typical for candidates to be hired at or near the top of the posted compensation range.
In addition to base salary, this role may be eligible for additional compensation such as variable incentives, bonuses, or commissions, depending on the position and applicable laws.
Capgemini offers a comprehensive, non-negotiable benefits package to all regular, full-time employees. In the U.S. and Canada, available benefits are determined by local policy and eligibility and may include:
- Paid time off based on employee grade (A-F), defined by policy: Vacation: 12-25 days, depending on grade, Company paid holidays, Personal Days, Sick Leave
- Medical, dental, and vision coverage (or provincial healthcare coordination in Canada)
- Retirement savings plans (e.g., 401(k) in the U.S., RRSP in Canada)
- Life and disability insurance
- Employee assistance programs
- Other benefits as provided by local policy and eligibility
