Staff AI Deployment Engineer, Australia
Salary not listed • Australia • Full-time
Posted 16d ago
About the job
Altana is seeking a Staff AI Deployment Engineer to join their Implementation team. This role is crucial for integrating Altana's AI-powered platform into customer businesses, transforming signed contracts into realized value. You will be responsible for standing up integrations, embedding Altana into customer processes, and ensuring the delivered insights drive decision-making. This is a technical role that requires close collaboration with customer stakeholders, as well as Altana's engineering, data science, and product teams, translating field learnings into future product development.
Responsibilities
- Design, build, and deploy agentic AI workflows, including multi-step agents, tool/function calling, and automations, to power customer business processes.
- Ground agents in customer context by connecting them to relevant data sources, APIs, and systems of record, defining their tools, permissions, and guardrails.
- Write and refine prompts, system instructions, and evaluation criteria to ensure agent reliability in production.
- Build orchestration and human-in-the-loop checkpoints for safe, auditable, and correctable agentic workflows.
- Monitor, evaluate, and iterate on deployed processes, tracking accuracy, cost, and latency, and tuning them based on evolving customer needs.
- Lead customer implementations end-to-end, including scoping, configuration, integration, validation, go-live, and support handoff.
- Configure and deploy Altana's platform capabilities against customer data and workflows, bridging the gap between product capabilities and customer needs.
- Build necessary integrations, data handoffs, automations, and internal tooling using AI-assisted development.
- Drive implementations to successful adoption, ensuring customer teams can use the system independently.
- Serve as the primary technical point of contact for customers during implementation, managing technical discussions and deep dives.
- Elicit and translate customer business problems into concrete technical scope.
- Communicate technical details and trade-offs to non-technical audiences and represent customer constraints to internal teams.
- Manage customer expectations, timelines, and scope effectively.
- Coordinate internal teams (engineering, data science, product, customer success) to ensure successful implementation delivery.
- Escalate and resolve issues by collaborating with other internal teams.
- Feed field learnings and identify product gaps back to the product organization.
- Develop reusable playbooks, templates, and documentation to improve implementation efficiency.
- Own the implementation roadmap for accounts, prioritizing work based on customer value and delivery risk.
- Write clear requirements, specifications, and acceptance criteria for engineering and data science teams.
- Prioritize tasks against customer value delivery timelines and cooperate with the product team on milestones.
- Define and track measurable success criteria for each implementation.
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 under deadlines.
- Strong communication and coordination skills, capable of running customer sessions and driving internal alignment.
- Product instincts, with an ability to identify the core problem and the smallest effective solution.
- Comfort with ambiguity and partial information, able to make progress before a complete picture is formed.
- Bias toward automation and repeatability over manual effort.
- Working proficiency in Python or a comparable language for scripting, debugging, and building integrations.
- Fluency with SQL for data exploration and validation.
- Practical experience with AI-assisted coding tools (e.g., Claude Code, Cursor, Copilot).
- Hands-on experience building or deploying agentic AI systems (LLM-powered agents, RAG, multi-agent orchestration).
- Skill in prompt engineering and agent evaluation.