Comfortable with numbers. Spreadsheets, dashboards, reading a report and asking why.
Can write a clean update that explains a result, not just reports a number.
Has built or tinkered with something of their own. A side project, a personal AI experiment, anything self-started. Doesn't need to be marketing related.
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POC & demonstrations by lead and validate complex Proof of Concepts (POC), technical demonstrations, and site surveys to ensure solutions align with customer requirements and business objectives.
Vendor compliance, manage the team’s vendor certification roadmap to ensure the organization maintains necessary partner statuses and meets all technical requirement thresholds.
Business growth by collaborating with product team to design marketing programs and involves in meeting and quarterly sales quotas like QBR through technical leadership.
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Continuous Improvement: Participate in shop-floor problem-solving tasks, 5S housekeeping activities, and lean manufacturing initiatives.
Cross-Functional Support: Coordinate with warehouse, logistics, and quality control teams to ensure smooth material flow and assembly line requirements.
Accelerate your path toward senior solutions engineering or product roles by owning measurable demo success metrics. Document technical wins and case studies that highlight your impact on deals and deployments.
Gain visibility with product and engineering teams by feeding back field insights that influence roadmap priorities. Learn how product decisions are made and how to translate customer requests into deliverable scope.
Drive proposal and tender quality - Author solution write-ups, bills of materials, and scopes of work that strengthen win rates, including submissions for regulated industries such as oil & gas.
Advise on IT and infrastructure integration - Guide client IT teams through deployment considerations including cloud vs. on-premise architecture, network and connectivity requirements, API integration, and dashboard/digital twin setup.
Build internal technical capability - Develop playbooks, FAQs, and training materials that raise the sales team's technical fluency and reduce dependency on ad hoc engineering support.
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Mentor engineers through code reviews, pair programming, and documentation to raise team standards and reduce defects. Set coding conventions, run knowledge-sharing sessions, and help junior engineers take ownership of operational tasks.
Build a visible portfolio of production systems by driving deployments, monitoring, and structured postmortems that show operational thinking. Own service-level metrics, alerting, and incident follow-up, and present outcomes to product and client stakeholders.
Take ownership of complex or escalated technical issues and drive them to resolution, staying hands-on rather than simply routing tickets to others.
Read and understand application code, logs, and system architecture well enough to trace an issue to its root cause, even without writing production code day to day.
Make simple, well-understood code changes or configuration fixes directly when it is safe and appropriate to do so, following the team's code review and testing standards.
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Accelerate your debugging and testing abilities through regular code reviews and hands-on bug fixes.
Ready to build useful software and ship things that matter? We're a small tech team focused on pragmatic AI-powered software for Malaysian businesses, and we're excited about working with us at THINK AI (M) SDN. BHD to make data and workflows simpler for real users.
As a Software Engineer you'll be a builder, turning product ideas into reliable backend services and web interfaces that can scale as our user base grows.
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Full Stack EngineeringFrontend: React, Next.js, TypeScript, modern component architectures, state management, real-time and streaming AI interfaces, agent activity and execution interfaces, data visualization.Backend: Node.js, TypeScript, Python, REST APIs, GraphQL, WebSockets and streaming, event-driven architectures, background workers, job queues, distributed systems, authentication and authorization.
Distributed SystemsDesign systems that reliably execute thousands or millions of AI and data-processing tasks. Kubernetes, Docker, Cloud Run and serverless, message queues, Redis, Kafka or equivalent, distributed job processing, concurrency management, rate limiting, retries, idempotency, fault tolerance, observability. You know how to build systems that stay reliable when agents fail, APIs time out, models hallucinate, or downstream services go away.
Data & Learning InfrastructureBuild the infrastructure agents need to learn from historical executions. PostgreSQL, BigQuery or equivalent data warehouses, ClickHouse or analytical databases, vector databases, embeddings, retrieval systems, event logs, feature stores, analytics pipelines, data ingestion.
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