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Altana is building an AI-powered platform for trusted global trade. The Implementation team owns the path from contract signature to realized customer value—standing up integrations, embedding Altana into customer business processes, and ensuring insights drive real decisions.
As a Staff AI Deployment Engineer, you will design, build, and deploy agentic AI systems that power customer business processes on real data. You'll ground agents in customer context by connecting them to data sources, APIs, and systems of record, then define the tools, permissions, and guardrails they operate within. You'll write and refine prompts, system instructions, and evaluation criteria to make agents reliable in production, build orchestration and human-in-the-loop checkpoints to keep workflows safe and auditable, and monitor, evaluate, and iterate on deployed processes—tracking accuracy, cost, and latency as customer needs evolve.
You'll lead customer implementations end to end: scoping, configuration, integration, validation, go-live, and handoff to support. You'll configure Altana's platform against real customer data and workflows, resolving gaps between product capabilities and customer needs. You'll build the connective tissue—API integrations, data handoffs, automations, and internal tooling—using AI-assisted development to move quickly. Your definition of done includes adoption: the customer's team is using it in their process without you in the room.
You'll serve as the primary technical point of contact through implementation, running working sessions and technical deep dives with stakeholders from analyst to executive. You'll elicit requirements from customers who can't fully articulate them yet, translate business problems into concrete technical scope, and communicate credibly in both directions—explaining platform behavior and trade-offs to non-technical audiences while representing customer constraints faithfully back to Altana.
You'll orchestrate internal effort behind each implementation, pulling in engineering, data science, product, and customer success at the right moments. You'll escalate and unblock, identify what only another team can solve, and feed field learning back into the product organization. You'll build reusable playbooks, templates, and documentation so the next implementation is faster.
You'll own the implementation roadmap for your accounts: sequence work by customer value and delivery risk, make explicit calls about what ships now versus later, write clear requirements and specs that engineering can build against, and define success in measurable terms.
REQUIREMENTS:
- 8+ years in customer-facing technical delivery (implementation, engineering, professional services, technical program management, or product management with hands-on scope)
- Demonstrated track record of shipping complex technical projects with external stakeholders on real deadlines with real consequences
- Strong communicator and coordinator; can run customer working sessions and drive internal alignment across teams
- Product instincts; naturally ask what problem this solves and what the smallest solution is
- Comfortable with ambiguity and partial information; make progress before the picture is complete
- Bias toward automation and repeatability over heroics
- Working proficiency in Python or comparable language; can read codebases, write and debug scripts, build integrations, and ship small features
- Fluency with SQL and comfort exploring and validating data
- Practical experience with AI-assisted coding tools (Claude Code, Cursor, Copilot, or similar) as a core part of workflow
- Hands-on experience building or deploying agentic AI systems (LLM-powered agents, tool and function calling, retrieval-augmented generation, multi-agent orchestration)
- Skill in prompt engineering and agent evaluation; can design prompts and evals, diagnose agent failures, and iterate toward reliable production behavior
- Familiarity with agent frameworks and protocols (MCP, LangChain, or similar) and 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 credible conversations with engineers
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)
