TC23 - Forward Deployed Engineer, AI and Business Transformation

We are hiring an FDE to work at the core of business transformation engagements within our Enterprise Transformation practice. You will conduct organizational diagnostics, redesign workflows and operating models, support technology adoption, and use AI tools to demonstrate value inside real client processes.

This is not an advisory role. The expectation is direct: you can design the solution and build the proof of concept that shows it working. Depending on the client engagement, this role may operate in a forward-deployed capacity — embedded inside a client team, working alongside their engineers, product leads, and operators to ship solutions in real time. That is not a separate track or program. It is a mode this work requires, and the expectation is that you are ready for it when the account calls for it

📌 Responsibilities: Process Engineering and Operating Model Design –Map current-state workflows end-to-end and diagnose root causes of operational friction— handoff failures, governance gaps, tooling mismatches, incentive misalignment –Redesign processes for target-state operations: leaner workflows, clearer decision rights, human-agent collaboration where AI accelerates throughput –Design operating models: org structure, roles, governance, cross-functional interaction models, Measurement and Value Realization –Establish performance baselines before engagements begin; build the measurement framework that tracks improvement over the engagement, not just at the end –Identify value gaps in stalled implementations: diagnose where technology deployment has outpaced business adoption and restored the path to ROI –Build reporting cadences that give clients a clear line from initiative to impact, AI, and Solution Building - This is a hard requirement, not a differentiator. We are not looking for someone who understands AI conceptually. We are looking for someone who builds with it. –Build working AI-powered prototypes directly: LLM APIs, platform-native features, no-code and low-code environments; you have shipped something functional –Use prototypes as the primary tool for stakeholder alignment — a working demo inside the client's actual process is more effective than a roadmap –Evaluate where AI changes a workflow and where it is a distraction; advise clients accordingly without overselling Technology Enablement and Rollout –Support rollout of new tools and workflows; ensure adoption is grounded in process design and change management, not just deployment –Design enablement materials that make new technology stick –Ensure technology implementations connect to the operating model they are designed to support Engagement and Practice Contribution –Support complex engagement scoping where client needs are ambiguous or span multiple services –Conduct diagnostic assessments: value capture, implementation readiness, adoption strategy –Contribute reusable frameworks and tools that improve how the practice qualifies and scopes work

📌 Must have –5-10 years in management consulting or equivalent roles at enterprise technology, SaaS, or professional services –Demonstrated experience in at least two of: organizational design, operating model transformation, change management, technology enablement, process engineering, or go-to-market execution –Track record of building structured, evidence-based arguments for senior stakeholders –Experience in organizational assessments, process redesigns, or large-scale technology rollouts is a strong differentiator

AI and Builder Capability Hands-on experience building AI-powered workflows, automations, or prototypes — you have shipped something functional, not just designed it Ability to scope and build a working proof of concept within the constraints of a client engagement — fast, focused, and designed to move a skeptical stakeholder Comfort working without a dedicated engineering team; you know when to build it yourself and when to pull in help

Platform and Martech Fluency – Working knowledge of the enterprise marketing technology landscape and where adoption typically breaks down organizationally – Familiarity with the Adobe ecosystem — AEM, Workfront, GenStudio, Firefly, Adobe Analytics, Marketo — is a strong differentiator

Mindset Diagnostician first: resist jumping to solutions before you understand exactly what is broken and why. – Accountable to outcomes, not deliverables — a good deck that does not change anything is a failure Biased toward early proof: You would rather build a working prototype than produce a roadmap that sits in a shared drive Most useful close to the work, inside the client team