AI Delivery Lead

At TeamStation AI, we are on a mission to bring together the brightest minds to solve tomorrow’s toughest technology challenges.

Our work is about more than just AI-it’s about building the future through collaboration and innovation.

We believe that the key to solving the world’s most complex problems lies in aligning diverse talents and perspectives. Our AI-powered platform enables cutting-edge scientific and technical teams to work smarter, faster, and together. By joining us, you’ll help unlock new technological breakthroughs and drive innovation where it matters most.

Join the Mission at TeamStation AI!

Where do we come from?

We are seeking visionaries, innovators, and problem solvers who thrive in fast-paced, collaborative environments. If you’re passionate about AI, technology, and solving critical challenges, we want to hear from you. Come be part of a team where your ideas can drive the future.

Location: LATAM | Remote

This Role Is Not for Most Project Managers

Most Project Management jobs are professional nagging. You chase people for status updates, move Jira tickets because somebody forgot, sit through meetings that should have been Slack messages, and spend more time managing process than helping teams ship. If your idea of delivery is running ceremonies, updating dashboards, and asking engineers for ETAs, you are going to hate this role.

We’re not looking for a project coordinator. We’re not looking for a Scrum Master. We’re not looking for somebody whose entire contribution is asking, “When will this be done?” We’re looking for somebody who can walk into a messy AI environment, figure out what is actually broken, remove friction, make hard calls, and keep teams moving.

What You’re Walking Into

Our TeamStation AI partner is the global leader in Out of Home (OOH) media technology.

For decades, outdoor advertising was largely guesswork. Companies bought billboards, transit placements, and physical advertising inventory with limited visibility into what actually worked. They changed that.

They built the technology layer that brings the same level of data science, machine learning, AI, and programmatic decision making used in digital advertising into the physical world. They process massive geospatial datasets, apply deep learning models, and help brands understand real world audience behavior at a scale that was not previously possible.

They are effectively building the operating system for physical advertising.

The engineering challenges are real. The data is complex. The AI systems are constantly evolving. That’s why this role matters.

What You’ll Own

Delivery Under Uncertainty

AI systems are messy. Models change. Data changes. Results change. Priorities change.

Your job is to keep teams shipping without waiting for perfect information or perfect conditions. You know how to create momentum when uncertainty is part of the environment.

Threshold Based Decision Making

AI products do not ship because they are perfect. They ship because risks are understood, managed, and accepted.

You will make decisions around:

• What ships
• What gets delayed
• What gets cut
• What risks are acceptable
• What risks are not

You can make decisions when information is incomplete instead of hiding behind endless analysis.

Protect Engineering Focus

Your job is to reduce noise, not create more of it.

You will:

• Control scope creep
• Remove blockers
• Align priorities
• Manage competing stakeholder demands
• Protect engineering time

You understand that every interruption has a cost.

Translate AI Complexity into Business Language

You can bridge the gap between technical teams and business stakeholders.

You understand concepts such as:

• Hallucinations
• Model reliability
• Inference costs
• Latency
• Evaluation frameworks
• Deployment tradeoffs

More importantly, you can explain them in business language without turning every conversation into a machine learning conference.

Improve Execution Without Bureaucracy

You believe in systems. You do not believe in process theater.

You create visibility, accountability, and operational clarity without burying teams under layers of unnecessary process.

Reliability Over Demos

Anyone can build a flashy demo. Production systems are different.

You care about:

• Reliability
• Observability
• Monitoring
• Evaluation quality
• Operational readiness
• Measurable business outcomes

You prioritize systems that work in production over demos that look good in meetings.

Tools & Environment

Project & Execution

• Jira
• Linear
• Notion
• Confluence

Communication

• Slack
• Zoom

Collaboration & Visualization

• Miro
• Lucidchart

Technical Environment

You should be comfortable working around:

• APIs and integrations
• AI and ML workflows
• Data pipelines
• Cloud infrastructure
• CI/CD environments

You do not need to build these systems yourself, but you need to understand how they work and where they fail.

The Non Negotiables

5+ Years Leading Technical Software Delivery

You’ve worked closely with engineering teams and understand how software actually gets delivered under pressure.

Experience Working in AI/ML or Data Heavy Environments

You do not need to be an ML engineer, but you must already understand the operational realities of AI products and data systems.

You understand the realities of:

• Imperfect models
• Hallucinations
• Data quality issues
• Evaluation thresholds
• Deployment tradeoffs
• Operational AI constraints

AI Tooling & Workflow Fluency

You actively use AI tools to improve execution, analysis, communication, prioritization, debugging, documentation, or operational workflows. We expect familiarity with modern AI-assisted work environments, not just passive exposure to ChatGPT.

You should already be comfortable experimenting with and integrating tools such as:

• ChatGPT
• Claude
• Gemini
• Cursor
• AI copilots
• AI-assisted coding tools
• AI-assisted research tools
• AI workflow automation tools

The key is not the tool itself. It’s whether AI has become part of how you operate and solve problems every day.

AI Operational Thinking

You understand concepts like:

• Hallucinations
• RAG systems
• Evaluation frameworks
• Model reliability
• Prompt iteration
• Deployment risk
• Latency versus accuracy tradeoffs
• Fallback mechanisms
• Observability and monitoring

You do not need to be an ML engineer, but these concepts should not be new to you.

Strong Decision Making Under Ambiguity

You can make hard calls when information is incomplete.

You do not default to:

“It depends.”

You evaluate risk, make a decision, communicate it clearly, and move forward.

Technical Fluency

You can comfortably discuss:

• APIs
• Integrations
• Data pipelines
• Deployments
• Cloud environments
• System constraints
• Reliability tradeoffs

You do not need to write production code, but you do need to understand the conversations.

Agile Without Process Addiction

You understand Agile well enough to know where it helps and where it gets in the way.

You value:

• Shipping
• Iteration
• Feedback loops
• Accountability
• Ownership
• Measurable outcomes

Over ceremonies, process, and status theater.

Radical Transparency

You surface problems early.

You communicate risk clearly.

You prefer uncomfortable truth over polished status reports.

Every time.

English Fluency

You can communicate effectively with:

• Engineers
• Product teams
• Data teams
• Executives
• Business stakeholders

Across distributed global teams.