How We Work — Our Operating Principles 

• Player-coach leadership. You and your squad leads build alongside the teams — writing code, reviewing models, shipping to terminals. When the person making decisions is also in the work, things move faster and outcomes get better. 

• Outcomes over technology delivery. Every engineer knows what changes at the terminal because of their work. Not sprints closed, not models deployed — vessels turning faster, revenue growing. 

• Every engineer is a force multiplier. AI handles the repetitive work so people spend their time on decisions that drive commercial and terminal outcomes. The multiplier isn't fewer people — it's people solving bigger problems. 

• Forward-deployed engineering. We build where the problem lives — embedded with planners and operators, feeding every terminal's fix back into the platform so it becomes a capability for the whole network. 

What Success Looks Like in Your First Year 

• Commercial AI products in the hands of commercial teams and moving revenue — with measured adoption, not just deployment. 

• The cloud migration of our core data platform completed with improved reliability and lower run cost. 

• Asset intelligence models informing real replacement and investment decisions, with finance and operations trusting the numbers. 

• Four squad leads operating as genuine player-coaches — growing, accountable, and capable of running the engine when you step back. 

• An engineering culture where AI-assisted development is the default and delivery speed is visibly compounding. 

What You Bring 

• Engineering leadership at scale. 14+ years in software/data/ML engineering, 5+ years leading multi-team organizations (25–50 engineers) through senior leads — with a track record of delivery in production, at scale, where the business depended on it. 

• Player-coach credibility. You still build. You can review an architecture, challenge a model design, or pair on a hard problem — and your teams know it. You coach squad leads to do the same. 

• Data platform depth. Hands-on history with modern cloud data platforms (AWS preferred — S3, Glue, EMR, Redshift, SageMaker, Bedrock or equivalents), large-scale migrations, data engineering, ML Ops and AI Ops, and platform observability. 

• Gen AI in production. You have shipped LLM-powered products — RAG systems, co-pilots, or agentic workflows — where quality, latency, cost, and evaluation were real engineering constraints, not demos. 

• Commercial and asset instinct. You connect engineering to money. Pricing, revenue intelligence, TCO modeling, lifecycle economics — you understand the decision your product enables, and you push back on poorly framed requirements. 

• People leadership that compounds. You hire well, grow leaders beneath you, have honest conversations early, and build teams that outlast you. You lead distributed teams across time zones with clarity and care. 

• Decision speed under ambiguity. You make the call, communicate it clearly, and keep multiple workstreams moving. Momentum over consensus-seeking.