Forward Deployed AI Engineer / Technical Consultant, Leiden

What You’ll Be Doing As a Forward Deployed Engineer / Technical Consultant, you’ll work directly with Nebul’s clients to design, build, and deliver AI solutions in real‑world environments.

This is not a traditional consultancy role where you only advise from the sidelines. You will be expected to deeply understand client problems, challenge assumptions, design pragmatic technical solutions, and work hands‑on with engineering teams to bring those solutions into production.

You’ll operate across discovery, architecture, implementation, and delivery. One day you may be working with a client to map business processes and identify automation opportunities; the next day you may be building an AI workflow, integrating with APIs, designing a RAG pipeline, or helping shape thetechnical architecture for a client‑specific deployment.

You’ll work closely with Nebul’s platform, AI, security, and commercial teams to ensure that solutions are technically strong, commercially relevant, secure, and scalable.

This role is ideal for someone who combines strong technical depth with the ability to communicate clearly with clients, understand business context, and turn ambiguity into working systems.

Key Responsibilities

Work directly with clients to understand their business processes, technical environments, challenges, and AI opportunities.

Translate client needs into clear technical solution designs, implementation plans, and delivery roadmaps.

Design and build forward‑deployed AI systems tailored to client‑specific environments, workflows, data sources, and constraints.

Develop AI‑native applications using LLMs, agents, retrieval, automation, tool calling, and workflow orchestration.

Build backend services, integrations, and orchestration layers using Python, Go, and where needed TypeScript.

Implement LLM‑powered workflows that integrate with client systems, APIs, databases, knowledge bases, and external tools.

Advise clients on practical AI adoption, including use‑case selection, feasibility, architecture, implementation approach, and production readiness.

Apply strong prompt engineering, model selection, and evaluation practices to ensure reliable and predictable system behavior.

Work hands‑on with RAG, embeddings, vector databases, LLM observability, tracing, evaluation, and cost monitoring.

Collaborate with Nebul’s internal engineering teams to align client solutions with Nebul’s sovereign cloud infrastructure and AI platform capabilities.

Support pilots, proof‑of‑value projects, and production deployments, while ensuring solutions are robust, secure, and maintainable.

Act as a trusted technical partner to clients, bridging the gap between business stakeholders, technical teams, and Nebul’s internal experts.

Continuously improve delivery methods, reusable components, best practices, and technical patterns for forward‑deployed AI projects.

What Your Day Will Not Look Like

Writing slide decks without being involved in implementation.

Only giving high‑level advice without building or delivering anything.

Building isolated AI demos that never reach production.

Working on traditional CRUD applications with limited AI or client impact.

Sitting far away from the client problem— this role is hands‑on, client‑facing, and outcome‑driven.

What You Bring Strong experience as a backend, full‑stack, solutions, platform, or AI engineer.

The ability to operate in a client‑facing role and communicate clearly with both technical and non‑technical stakeholders.

Hands‑on experience with Python, plus working knowledge of Go and/or TypeScript.

Experience building applications or workflows that integrate with LLMs, APIs, data sources, and external systems.

A strong understanding of AI system design, agentic workflows, RAG, prompt engineering, and automation.

The ability to translate ambiguous business problems into practical technical solutions.

Comfort working in complex client environments with real‑world constraints around security, data, systems, and adoption.

A pragmatic engineering mindset: you know when to prototype, when to harden, and when to simplify.

Strong ownership, curiosity, and the ability to drive work forward independently.

The ability to build trust with clients while staying technically sharp and delivery‑focused.

Bonus Points If You Have

Experience in technical consulting, solution architecture, customer engineering, or forward‑deployed engineering.

Experience deploying AI systems on cloud‑native or Kubernetes‑based platforms.

Familiarity with sovereign cloud, data security, compliance, or regulated environments.

Experience with LangChain, LlamaIndex, PydanticAI, Langfuse, vector databases, or similar tools.

Experience designing or implementing RAG pipelines, agentic systems, or internal AI tooling.

Experience working with enterprise clients, complex integrations, or production AI deployments.

Experience mentoring engineers or acting as a technical lead.

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