Portcast is a venture-backed, Singapore-based logistics technology startup building a real-time transportation visibility platform for global supply chains. We help shippers, manufacturers, and logistics service providers turn data into decisions and decisions into measurable business impact.
Our platform goes beyond visibility. Portcast enables action at scale by surfacing the right risks early, helping teams prevent detention and demurrage, accelerate exception management, and close invoices faster with built-in evidence. We turn visibility into outcomes: reduced costs, improved operational control, and more predictable supply chains.
Founded in 2018 and backed by leading technology investors, we are building for an industry at a critical inflection point of digital transformation. Our team of software engineers, data scientists, and logistics experts is on a mission to make supply chains not just visible, but decisively actionable end-to-end.
ABOUT THE ROLE:
We’re looking for a Technical & AI Director to lead our engineering, ML/DS, and analytics teams while staying close to architecture and execution.
This role will lead and manage the Engineering team, ML/Data Science team, and Data Analytics team. You’ll drive how we build, how we use data and AI, and how our systems scale as the company grows.
We’re looking for a strong technical leader who thinks from first principles, makes clear decisions, and stays close to the technology. Someone who can step into complex problems, break them down clearly, and guide teams toward practical, scalable solutions.
You’ll work closely with the CEO, Product, Sales, and Customer teams to translate real customer problems into systems, tools, and AI capabilities that drive outcomes, value to our customer and company revenue, not just features.
This is not a pure management role. It requires strong technical judgment, comfort operating across engineering and data, and the ability to lead through both direction and execution. You’ll set the technical bar, strengthen how teams work together, and ensure engineering, ML, and analytics move as one cohesive capability supporting the business.
- Engineering, ML, and Analytics operate as a clear, structured, and high-performing unit with defined ownership and strong delivery predictability.
- Technical decisions are grounded in first-principles thinking, scalable architecture, and long-term product clarity.
- AI and data capabilities are consistently deployed into production and directly tied to measurable business impact.
Engineering Leadership & Architecture
- Own technical direction across platform, infrastructure, and product systems
- Define scalable architecture and ensure reliability, performance, and security
- Improve engineering velocity, code quality, and delivery discipline
- Strengthen documentation, ownership, and system clarity
- Mentor tech team: Engineering, ML, Analytics
AI, Data Science & Analytics Strategy
- Define how AI and data drive competitive advantage in our product
- Ensure ML models move from experimentation into production environments
- Strengthen MLOps, data pipelines, and real-time data systems
- Align analytics insights with product and customer decision-making
- Ensure AI investments translate into real outcomes, not just experiments
Product & Business Alignment
- Partner closely with Product to shape technical roadmap
- Work with Sales and Customer teams to understand enterprise requirements
- Guide solution design for complex customer use casesEnsure engineering effort maps directly to business impact
Team, Culture & Organizational Development
- Lead and develop Engineering, ML/Data Science, and Analytics teams
- Hire strong senior talent and raise the technical bar
- Create clarity in roles, responsibilities, and technical ownership
- Reduce single-point dependency across critical systems
- Build a culture of accountability, curiosity, and effectiveness
Hands-On Technical Leadership
- Stay close to architecture and key system decisions
- Review critical designs and technical proposals
- Step into complex technical problems when needed
- Prototype or validate high-impact ideas
- Lead by example in technical depth and problem solving
- 10+ years in software engineering, data, or ML environments
- Bachelor’s, Master’s, or PhD in Computer Science, Engineering, or a related field.
- Experience leading technical teams in a startup or product-driven B2B SaaS company
- Track record building and scaling production AI/ML systems
- Experience with distributed systems, real-time data, and cloud infrastructure
- Background in logistics or supply chain industry, or data-heavy SaaS is a strong plus
- Previous experience working in enterprises/fortune 500 is a plus
Technical Depth
- Curious by Default: You dig deeper instead of following instructions to the letter. When something in the system surprises you, you chase the why, not just the fix. You treat every layer of the stack as something worth understanding, and you ask the good questions early.
- Strong backend and system architecture expertise
- Experience deploying ML/AI systems into production, not just research
- Deep understanding of MLOps, data platforms, and model lifecycle management
- Comfortable operating across engineering, data science, and analytics domains
Leadership & Culture
- First-principles thinker who breaks down complex problems clearly
- High ownership and accountability
- Builder mindset with strong technical judgment
- Clear communicator across technical and business teams
- Focused on effectiveness and real outcomes
- Globally distributed, remote-first flexibility: Work with a fully distributed team across Asia and Europe, built on trust, accountability, and collaboration. Our diversity of perspectives fuels innovation and keeps us curious.
- Tech-first team: You’ll work with like-minded individuals who share a passion for solving difficult problems using technology.
- Accelerated growth: Compress the learning curve in a couple of years by owning the web app from day one as your own baby. We are building our company to be the next B2B market leader in predictive global supply chains and you’ll be a major part of our story.
- Impact you can see: With a lean structure, your work is effective from the start. You’ll see the results of your ideas and decisions directly moving the business forward.
Curiosity: We read the code before we trust it. We dig into why a system behaves the way it does, not just how to make the error go away.
Ownership: We act like founders. We don't wait for a ticket to fix what's broken, and we stay on a problem until it's actually solved in production.
Raising the bar: We do not settle for code that only works locally or solves the immediate request. We aim for backend systems that are reliable, scalable, maintainable, and easy for the team to reason about.
Effectiveness: We focus on engineering work that creates real product and customer impact. We prioritize the right problems, make practical trade-offs, and ship solutions that improve outcomes, not just output.
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
