Senior Platform Engineer

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.

Role Context

TeamStation AI is building a Senior Platform Engineer role for our platform partner within its Data and Analytics engineering organization. Mission is direct: own meaningful software outcomes from an ambiguous business problem through architecture, delivery, production operation, and continuous improvement.

AI has changed the amount of ground one strong engineer can cover. Right person will use AI coding agents and modern engineering tools as part of daily workflow, while keeping human judgment, technical standards, security, observability, and production accountability firmly in control.

Role operates as a senior individual contributor position with no direct reports. Influence comes from what gets shipped, standards created, technical reasoning documented, and leverage added to wider engineering organization.

What You Will Own

Complete Software Outcomes

• Turn incomplete requirements into working systems. Own problem definition, architecture, implementation, testing, deployment, production readiness, and operational support.
• Make clear technical decisions across scope, architecture, risk, and delivery. Explain tradeoffs in basic language so engineering and business partners can understand reasoning and move with confidence.
• Move across unfamiliar systems, languages, and domains when objective requires it. Learn fast, find signal, and avoid waiting for perfect context before making responsible progress.

AI Assisted Engineering

• Use AI coding agents and modern development tools to improve delivery speed, technical coverage, testing, documentation, and system understanding.
• Know where AI creates leverage and where supervision is mandatory. Review generated code using same standards applied to human written code, including correctness, security, maintainability, observability, performance, and operational risk.
• Help establish practical digital rails for AI assisted software delivery. Goal is not more code. Goal is better digital output, with clear human judgment and governance around it.

Platform Quality and Delivery

• Build and maintain software across front end, back end, data, infrastructure, and platform concerns as work demands.
• Create strong automated testing, CI/CD pipelines, deployment controls, release processes, monitoring, alerting, and recovery paths so higher throughput does not create hidden production risk.
• Own services in production, participate in on call operations and incident response, find root causes, document decisions, and improve system after failures.
• Leave clear technical trail. Architecture, operating procedures, dependencies, decisions, and recovery steps must remain understandable to engineers who were not present when system was built.

Required Experience

Engineering Breadth

• Strong software engineering experience across more than one domain, including front end, back end, data, cloud infrastructure, or platform engineering. Deep expertise in every area is not required, but ability to become effective across full delivery path is required.

AI Development Fluency

• Hands on experience using AI coding agents or AI assisted development tools in real engineering work. Candidates must explain how these tools changed their workflow, where they improved output, where they introduced risk, and how generated work was validated.

Production Ownership

• Direct experience designing, shipping, operating, and improving production systems. Strong understanding of observability, incident response, testing, deployment safety, performance, maintainability, and recovery.

Delivery Automation

• Hands on experience building or maintaining CI/CD pipelines that automate testing, delivery, deployment, and quality controls.

Learning and Judgment

• Evidence of learning unfamiliar technologies quickly, reasoning through ambiguity, making responsible tradeoffs, and turning incomplete context into working software without losing control of quality.

Communication and Accountability

• Clear written and verbal communication. Strong ownership of outcomes, commitments, documentation, and follow through. Ability to explain hard technical decisions simply and bring other people into reasoning.

Preferred Experience

• Experience working as a generalist within a small engineering team where individual ownership and output are high.
• Experience spanning multiple technical domains, such as web and data, back end and infrastructure, or application engineering and cloud platforms.
• Experience setting engineering standards, improving developer workflows, or creating leverage for other engineers through automation, platform capabilities, documentation, and reusable systems.

Operating Profile

Mental shape matters. Right engineer is curious, systematic, calm under ambiguity, and accountable when work reaches production. Strong opinions are welcome when they come from evidence and can change when better evidence appears.

We need a builder who stays close to code, sees system around code, and understands that AI speed without engineering judgment creates noise and risk. Quality, throughput, governance, and business outcomes must work together.

Success Measures

Success means complete outcomes reach production safely, AI tools increase useful engineering capacity without lowering standards, systems remain observable and maintainable, delivery becomes easier to repeat, incidents produce learning, and technical decisions remain clear enough for wider team to build on.

Must Haves

• Senior software engineering experience across at least two domains: front end, back end, data, cloud infrastructure, or platform engineering
• Hands on use of AI coding agents or AI assisted development tools in production engineering workflows
• Proven ownership from ambiguous requirement through architecture, delivery, deployment, and production operation
• Experience owning production services, including observability, incident response, root cause analysis, and recovery
• Hands on experience building or maintaining CI/CD pipelines
• Strong automated testing, code review, security, maintainability, and operational quality practices
• Fast learning ability across unfamiliar languages, systems, and technical domains
• Clear written communication, technical reasoning, documentation, and accountability for outcomes
• Ability to work as a senior individual contributor with high autonomy and no direct reports