How Forward Deployed Engineering works at Taktile, and what our team is working on.
Forward Deployed Engineering has become the hottest role in tech. VCs are writing think-pieces about it, every AI startup is hiring for it, and everyone seems to have a slightly different definition of what it actually is.
Here’s ours: FDEs are engineers who own the customer’s outcome, not just the code.
Our customers don’t buy software, they’re buying outcomes: claims paid in minutes instead of weeks, credit decisions made without a queue, new customers onboarded faster than competitors. An FDE’s job is to take an enterprise from a signed contract to these high-impact results running in production. They also ensure the customer can own and evolve each outcome long after we step back.
This post covers what FDE means at Taktile, how our team works, what we’ve learned deploying AI decisioning inside some of the world’s largest financial institutions, and what kind of person thrives in our team.
What is an FDE?
Before the role “FDE” became known, many large SaaS organizations used various flavors of implementation engineers to ensure their software was deployed and deeply embedded in enterprise organizations. In the 2010s, Palantir really leveraged this model, placing their engineers as close to the problems as possible. They would often spend months working directly with their customers on a factory floor.
The AI shift accelerated this further. After ChatGPT’s release in ’23, businesses raced to move beyond RAG pilots and chatbots into production, but roughly 95% couldn’t make that leap alone. The fix looked a lot like Palantir’s: embed technical, resourceful engineers directly with customers to go deep into their problems and shorten the feedback loop. OpenAI, Anthropic, Ramp, and Databricks now all run their own version of this motion.
At Taktile, we define an FDE as an engineer working directly with customers to deliver an outcome that solves a complex problem.
FDE at Taktile
Taktile is the Agentic Decision Platform for critical decisions in financial services. Our customers combine AI agents, deterministic decision flows, case management, and humans in the loop to automate and optimize high-stakes decisions across onboarding, SMB credit underwriting, and claims processing. Getting those decisions live inside a large financial institution - with its legacy systems, regulated processes, and risk teams - is exactly the problem FDEs exist to solve.
FDE at Taktile has an unusual origin story. Taktile’s earliest customer-facing engineers were FDEs. As the company grew, the role evolved into one more similar to a Solutions Engineer, covering both pre- and post-sale stages of the customer lifecycle. With the shift towards AI and enterprise, it became obvious we needed a dedicated, deeply technical function running implementations that solve our customers’ problems and deliver value from the Taktile product — engineers accountable for getting customers live, not just supporting a sale. So we brought the FDE back.
Today we’re a team of more than 20 FDEs across Europe and the US. We have FDEs in Berlin, London, New York, San Francisco, Munich and São Paulo.
What the work looks like
Pre-sale
Shape the plan and run a proof of value
Architect
Design scalable, fault-tolerant architecture
Integrate
Add APIs, databases and other data sources to enrich decisions
Build & enable
...and upskill our customers
Validate
Ensure testing passes, and accuracy is high
Go-live
Clean handover to the customer
FDEs own the technical delivery of customer implementations end to end, with one clear objective: getting the customer live and delivering value in production. Their involvement often begins while the customer is still a prospect, working closely with Solutions Engineers to turn the proposed solution into a practical implementation plan. This early continuity reduces handover friction and accelerates delivery once the project begins.
The clearest way to show the shape of the job is through the work itself.
AI Document Extraction
A large US Fintech using Taktile to manage their document extraction pipeline with LLMs had seen their accuracy results plateau short of expectation.
Rather than fine-tuning and making small adjustments around the edges, our FDEs worked with the customer and rebuilt the evaluation approach from the ground up. This enabled them to run detailed experiments across different patterns for the end-to-end pipeline, increasing accuracy into the high 90th percentile and far exceeding their initial expectations.
Enterprise Transformation
Working closely with a large insurance provider, our FDEs partnered across a wide network of stakeholders in the claims, risk, compliance, operations and engineering teams to tackle one of the largest transformation projects we’ve seen and automate claims processing.
Being at the frontier of what’s possible with AI in a regulated environment, our FDEs turned ambiguity and complexity into a production system the customer now points to as proof of what’s possible.
From Sketch to System
A banking-infrastructure provider brought us an early, high-level architecture for a new lending product, with plenty of open questions still to work through.
Rather than waiting on a fully specced-out ask, our FDEs used their knowledge and experience to build out the logic, documenting every assumption along the way, and presented it back as a working demo with the outstanding questions for discussion.
The customer used the demo to help win another client, and the engagement went on to show the full arc of the role.
FDEs design architectures that are scalable, fault-tolerant, and maintainable. They help to establish a data model and the ontologies needed to learn from historical decisions and improve future decision logic, and integrate the customers’ APIs, databases, and external data sources.
Depending on the project’s complexity, often FDEs will both build decision logic directly and upskill and enable a customer’s team to do so. We’re not a consultancy that builds forever. Our customers’ risk and ops teams ultimately own their decision logic, so every implementation is also a teaching engagement so that they can own and improve this in the future.
Once the decision logic is fully implemented, FDEs lead rigorous testing and validation across accuracy, reliability, compliance, and operational readiness—ensuring the solution delivers the right outcomes and can move safely into production.
Our principles
We run the team on four core principles. They all come down to the same thing: we win when our customers win.
Accelerate time-to-value
Every week of implementation is a week the customer isn’t seeing results. We pre-build before kickoff, scope hard, and take the fastest safe path to production, bringing our domain and technical expertise to support our customers.
Deliver a superior experience
Enterprise implementations are stressful. We take on that stress instead of adding to it: we adapt to each customer’s way of working and fix problems instead of debating whose fault they are. And we enable customers to further develop solutions themselves.
Shape the product
We hit product gaps before anyone else, because we hit them in production. Every workaround we build for one customer gets fed back to the product team so the next one doesn’t need it.
Work as a team
No lone heroes. Weekly kickoffs, knowledge shares, and problem-solving sessions where anyone can bring a hard problem. What we learn on one project becomes the playbook for the next. Staffing junior team members with experienced FDEs helps with individual development.
The AI part (it’s most of the job now)
Two things are true at once: FDEs at Taktile use AI heavily to assist with our work, and our work is deploying large language models in a safe and reliable way.
On the first: everyone on the team uses Claude Code and Cursor daily, and we’ve built a library of our own internal agents that automate repetitive parts of implementations. FDEs also contribute towards the wider organization’s uptake of AI, whether through helping other teams implement agents, or sharing the latest relevant studies on AI in enterprise.
On the second — and this is what makes the role distinctive — Taktile FDEs run AI/ML projects for enterprises, end to end. This means designing agentic workflows, building evaluation datasets, defining confidence methodology, collecting ground truth, iterating on model behavior, and putting guardrails around LLMs so they can be trusted with regulated decisions. Because our customer-facing agents are built inside Taktile, we work on top of real infrastructure for testing, iteration, guardrails, and workflows — not duct tape and prompt spreadsheets.
Few businesses are shipping agentic AI into production at banks and insurers. If you want to build not just a demo but a full production system — this is one of the best seats available.
The honest hard parts
This role is not for everyone, and we’d rather tell you now:
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Operating at enterprise scale requires focus. Large organizations balance complex systems, regulatory requirements, and many competing priorities. FDEs help to bring pace, organization and ownership, enabling enterprise teams to align around the highest-impact opportunities, making pragmatic trade-offs, and moving quickly and safely from idea to production.
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You’ll hold a lot of context. Data models, decision logic, integrations, complex domains, customer org charts, product limitations, and timelines — often across more than one implementation at once.
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Challenging problems. Much of what we deploy has genuinely never been done before. We’re at the forefront of applied AI in regulated industries, which means experimentation, ambiguity, and often being the first to solve problems nobody has written about yet. If that sentence excites you rather than scares you, keep reading.
How we hire FDEs
We look for four key traits during the interview process:
Technical Excellence
You can design, build, and debug production systems. We value judgment and action over perfection.
Great Communication
You can read the room, understand concerns, align teams, lead workshops, and translate between risk policy and code.
Business Acumen
You can read how an organization balances risk and reward, and translate the context into priorities and delivery trade-offs.
Product Sense
You can identify what is worth doing, make pragmatic trade-offs, and turn ambiguous problems into measurable outcomes.
Conclusion
The next generation of B2B AI companies will be powered by engineering teams that are as customer-focused as they are technically strong. At Taktile, FDEs sit exactly at that intersection: deploying AI decisioning inside the world’s largest financial institutions, and enabling those institutions to fully own their solutions.
We’re a team of more than 20 people across six cities. The systems we ship decide millions of real outcomes for real people every day: claims paid, loans approved, customers onboarded. If you want your engineering work to impact this directly, we’re hiring.