Staff AI Deployment Engineer, Amsterdam

Altana is the network for trusted trade. Our AI-powered product network empowers governments and businesses to build a more resilient and secure global economy while keeping trade flowing.

The Opportunity at Altana

The Implementation team is the tip of the spear in getting Altana's platform into production inside our customers' businesses. We own the path from signed contract to realized value: standing up integrations, wiring Altana into the customer's existing business processes, and making sure the insight we deliver actually changes decisions on the customer's side.

We are a delivery organization with a technical core. We work directly with customer stakeholders — from trade compliance analysts to enterprise IT — and we work just as closely with Altana's engineering, data science, and product teams to translate what we learn in the field into what gets built next.

You Will

Design and deploy agentic AI systems

Design, build, and deploy agentic AI workflows — multi-step agents, tool and function calling, and automations — that power the customer's business processes and act on real customer data

Ground agents in customer context: connect them to the right data sources, APIs, and systems of record, and define the tools, permissions, and guardrails they operate within

Write and refine the prompts, system instructions, and evaluation criteria that make an agent reliable enough to trust in production

Build the orchestration and human-in-the-loop checkpoints that keep agentic workflows safe, auditable, and correctable in a customer's environment

Monitor, evaluate, and iterate on deployed processes — tracking accuracy, cost, and latency, and tuning them as the customer's data and needs evolve

Own technology implementation and delivery

Lead customer implementations end to end: scoping, configuration, integration, validation, go-live, and handoff to steady-state support

Configure and deploy Altana's platform capabilities against real customer data and real customer workflows, resolving the gap between what the product does today and what this customer needs

Build the connective tissue — API integrations, data handoffs, automations, and internal tooling — that makes an implementation work, using AI-assisted development to move quickly

Drive implementations to a definition of done that includes adoption, not just deployment: the customer's team is using it, in their process, without us in the room

Engage customers as a trusted technical partner

Serve as the primary technical point of contact through implementation — running working sessions, demos, or technical deep dives with stakeholders from analyst to executive

Elicit requirements from customers who often can't fully articulate them yet, and translate business problems into concrete technical scope

Communicate credibly in both directions: explain platform behavior, data quality realities, and trade-offs to non-technical audiences, and represent customer constraints faithfully back to Altana

Manage expectations, timelines, and scope with the directness that keeps large enterprise programs on track

Coordinate across internal teams

Orchestrate the internal effort behind each implementation, pulling in engineering, data science, product, and customer success at the right moments and keeping them aligned on a single plan

Escalate and unblock: identify what only another team can solve, make the ask precisely, and follow it to resolution

Feed field learning back into the product organization — patterns across customers, recurring friction, gaps worth building

Build reusable playbooks, templates, and documentation so the next implementation is faster than the last

Apply product management judgment

Own the implementation roadmap for your accounts: sequence work by customer value and delivery risk, and make explicit calls about what ships now versus later

Write clear requirements, specs, and acceptance criteria that engineering and data science can build against

Prioritize against customer value delivery timelines and cooperate with the product team on milestones and deadlines

Define what success looks like for each implementation in measurable terms, and track against it

You Have

8+ years in customer-facing technical delivery — implementation, engineering, professional services, technical program management, or product management with hands-on scope

A demonstrated track record of shipping complex technical projects with external stakeholders, on a real deadline, with real consequences

Strong communicator and coordinator: you can run a customer working session in the morning and drive internal alignment across three teams in the afternoon

Product instincts — you naturally ask what problem this actually solves and what the smallest thing that solves it is

Comfortable with ambiguity and partial information; you make progress before the picture is complete

Bias toward automation and repeatability over heroics; you'd rather fix the process than run it manually again

Technical competencies we expect

Working proficiency in Python or a comparable language: you can read a codebase, write and debug scripts, build integrations, and ship small features — you don't need to be a systems engineer

Fluency with SQL and comfort exploring and validating data to answer your own questions

Practical experience with AI-assisted coding tools (Claude Code, Cursor, Copilot, or similar) as a core part of how you work — you use them to move faster on unfamiliar codebases, prototype integrations, and automate the repetitive parts of delivery, and you know how to review what they produce

Hands-on experience building or deploying agentic AI systems — LLM-powered agents, tool and function calling, retrieval-augmented generation, or multi-agent orchestration

Skill in prompt engineering and agent evaluation: you can design prompts and evals, diagnose why an agent fails, and iterate toward reliable production behavior

Familiarity with agent frameworks and protocols (e.g., MCP, LangChain, or similar) and with wiring agents to external tools, APIs, and data sources

Familiarity with APIs (REST, Swagger/OpenAPI), Git-based workflows, and cloud-hosted systems

Enough understanding of data pipelines, data modeling, and ML systems to scope work, diagnose issues, and hold a credible conversation with the engineers who build them

Nice to Have

Experience with global trade, supply chain, customs, or trade compliance

Background in enterprise software implementation at scale (multi-stakeholder, multi-quarter programs)

Exposure to Spark, Databricks, or large-scale data processing

Experience building internal tooling or automation that a team adopted

Formal product management experience — roadmaps, specs, stakeholder prioritization

Technologies in Our Environment

Languages: Python, SQL, Spark

AI tooling: Claude Code and other AI-assisted development tools, used daily

Agentic AI: LLM agents, tool and function calling, MCP, retrieval-augmented generation, and agent orchestration frameworks

Platform & infrastructure: Docker, Git, Kubernetes, Swagger/OpenAPI, AWS, Azure

Datastores: Databricks, Elasticsearch, Postgres, Redshift

This role is based in Amsterdam, with flexibility to travel onsite to our customer’s locations

Our Values

Our values are the core beliefs that shape who we are, what we stand for, and how we behave.They form the foundation of Altana’s culture and integrity and guide how we hire, design, build, and connect with each other and our customers.

Trust: Our customers and partners entrust us with missions of the highest importance. We honor that by keeping our word, meeting commitments, and ensuring every action we take reinforces confidence in us. We rely on each other to deliver, to speak openly, and to hold ourselves accountable.

Resilience: In a world of uncertainty and complexity, our work must wi