Staff AI Deployment Engineer

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