Forward Deployed Engineer

ADEL; Amsterdam; On-site; EUR 70,000 - 110,000; Full time

Job summary

ADEL is hiring for a delivery-focused role to bridge business needs and hands-on engineering. You will investigate workflow requirements, decide what is worth building, write the software and help customers take ownership.

This is a delivery role where a convincing demonstration is just the starting point. You will collaborate with users and tech leaders to define problems, build AI-enabled workflows, connect data with proper access controls and design evaluation datasets to measure success.

Qualifications

• Proven ability to ship and support real software with demonstrable engineering contribution.
• Strong coding, debugging and testing skills in Python, TypeScript or a comparable production language.
• Practical understanding of LLM applications, evaluation and the limits of probabilistic systems.
• Confidence working with APIs, data stores, version control and cloud deployment practices.

Responsibilities

• Collaborate with users and technical leaders to define problems, constraints and success criteria.
• Build AI-enabled workflows, services and interfaces aligned to customer environments.
• Connect enterprise data and APIs with access controls and retrieval design.
• Create evaluation datasets and tests; measure quality, latency and cost.
• Move increments through code review, security checks, deployment and monitoring.
• Explain architecture choices and risks, raising dependencies when needed.
• Leave behind documentation, runbooks and practical coaching for customers.

Skills

Python

TypeScript

Debugging

Testing

Tools

APIs

Version control

Cloud deployment

Job description

An ADEL FDE connects business context with hands-on engineering. You will investigate the workflow, decide what is worth building, write the software and help the customer take ownership. This is a delivery role: a convincing demonstration is a starting point, not the finish line.

What you will do

• Work with users and technical leaders to define the problem, constraints, baseline and evidence of success.
• Build AI-enabled workflows, services and interfaces using the models and tools that fit the customer’s environment.
• Connect enterprise data and APIs with appropriate access controls; design retrieval, tool use and human review where they are useful.
• Create evaluation datasets and tests that expose failure modes. Measure task quality, reliability, latency and operating cost.
• Move working increments through code review, security checks, deployment and monitoring with the customer’s engineers.
• Explain architecture choices and trade-offs clearly. Raise dependencies and risks while there is still time to act.
• Leave behind documentation, runbooks and practical coaching so the customer can maintain and extend the work.

What you bring

• Evidence of shipping and supporting real software, with clear examples of your own engineering contribution.
• Strong coding, debugging and testing skills in Python, TypeScript or a comparable production language.
• Practical understanding of LLM applications, evaluation and the limits of probabilistic systems.
• Confidence working with APIs, data stores, version control and cloud deployment practices.
• Ability to discover the real need behind a request and communicate with both engineers and business stakeholders.
• Sound judgement about privacy, security, human oversight and when a simpler non-AI solution is better.