The Opportunity at Altana

The AI Deployment 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, deploying agentic AI 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. This role leads that team.

You Will

Lead and grow a team of AI Deployment Engineers

• Hire, coach, and grow a team of AI Deployment Engineers — set expectations, give direct feedback, and develop careers
• Own team capacity and staffing across a portfolio of customer implementations, matching engineers to accounts by skill, delivery risk, and development goals
• Establish the team’s operating rhythm — planning, reviews, and retrospectives that keep multiple implementations on track at once
• Build a culture of ownership, technical rigor, and adoption-focused delivery, where “done” means the customer is using it in their process, without us in the room
• Create the conditions for the team to move fast safely: shared playbooks, reusable tooling, and clear standards for deploying agentic systems in production

Own the mission across your portfolio

• Own delivery outcomes across every account your team touches: agentic AI in production, adoption, and changing decisions on the customer's side
• Set and defend the roadmap across implementations — sequencing by customer value and delivery risk, and making explicit calls about what ships now versus later
• Define what success looks like for each engagement in measurable terms, and hold the team accountable to it
• Step into the hardest implementations yourself when they need senior technical judgment or a steady hand with the customer

Set the technical bar for agentic deployment

• Define the team’s standards for designing, deploying, and operating agentic AI systems — prompts, evaluations, tool integrations, guardrails, orchestration, and human-in-the-loop controls
• Review and raise the quality of agent designs and deployments across reliability, safety, auditability, cost, and latency in real customer environments
• Stay hands-on enough to review what the team ships, unblock deep technical problems, and hold a credible conversation with Altana’s engineering and data science teams
• Codify what works into repeatable patterns so each deployment is faster and safer than the last

Engage customers as a senior technical partner

• Serve as the senior technical relationship owner across your accounts — from analyst to executive — and the escalation point when implementations get hard
• Translate ambiguous business problems into agentic solutions, and set realistic expectations on timeline, scope, and what AI can and can’t do reliably today
• Represent customer constraints faithfully back to Altana, and platform and data-quality realities credibly back to the customer

Coordinate across internal teams

• Orchestrate engineering, data science, product, and customer success behind your team’s implementations, keeping everyone aligned on a single plan
• Escalate and unblock precisely: identify what only another team can solve, make the ask, and own it to resolution
• Feed field learning back into the product organization — patterns across customers, recurring friction, and gaps worth building into the platform

You Have

• 8+ years in customer-facing technical delivery, including 2+ years leading engineers or technical delivery teams — implementation, solutions/forward-deployed engineering, professional services, technical program management, or engineering management
• A track record of building and growing high-performing technical teams that ship complex work for external stakeholders, on real deadlines, with real consequences
• A player-coach mindset: you lead the team and can still go deep technically when it matters
• Strong communicator and coordinator at every altitude — you can coach an engineer, run an executive conversation, and drive internal alignment across three teams in the same day
• Product and portfolio instincts — you prioritize across competing demands and say no, or “not yet,” with a defensible rationale
• Comfortable with ambiguity and accountable for outcomes before the picture is complete
• Bias toward automation, standards, and repeatability over heroics

Technical competencies we expect

• Enough hands-on depth to set the bar: you’ve built or deployed agentic AI systems yourself — LLM-powered agents, tool and function calling, retrieval-augmented generation, or multi-agent orchestration — and can review what your team produces
• Fluency in prompt engineering and agent evaluation, and the judgment to know when an agentic system is reliable enough for production
• Working proficiency in Python or a comparable language, and comfort with SQL, APIs (REST, Swagger/OpenAPI), Git-based workflows, and cloud-hosted systems
• Practical experience with AI-assisted coding tools (Claude Code, Cursor, Copilot, or similar) and with agent frameworks and protocols (MCP, LangChain, or similar)
• 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
• Experience scaling a delivery, professional services, or forward-deployed engineering team
• Background in enterprise software implementation at scale (multi-stakeholder, multi-quarter programs)
• 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 Australia, with flexibility to travel onsite to Canberra hub location