Responsibilities

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.
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

Qualification

Strong communicator and coordinatorTechnical competencies we expectExperience with global tradeExposure to SparkTechnologies in Our EnvironmentLanguagesAI tooling

Required

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

Preferred

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

Staff AI Deployment Engineer, Singapore at Altana AI · LumiJobs

Staff AI Deployment Engineer, Singapore

Responsibilities

Qualification

Required

Preferred

Benefits

Company

Altana AI

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