Client-Embedded Engineer for Vault Integrations (Python/Go)

THOUGHT MACHINE PTE. LTD.; Singapore; On-site; SGD 140,000 - 210,000; Full time

Benefits offered by this job

Healthcare

25 days holiday and public holidays

Competitive maternity and paternity-le

Job summary

Thought Machine is hiring Forward Deployed Engineers to embed with client engineering teams, delivering production-ready Vault integrations. You will code across backend and tooling, troubleshoot distributed systems, and drive technical success for engagements.

The role requires travel to client sites up to 25% of time, hands-on coding in Python or Go, and strong cloud and Kubernetes experience to ensure robust, scalable deployments for financial-grade environments.

Qualifications

• Solid hands-on experience with Python or Go and ability to iterate from requirements to delivery.
• Strong knowledge of Linux/Unix environments and container orchestration (Kubernetes).
• Experience deploying and operating workloads on at least one major cloud platform (AWS, GCP, or Azure).
• Ability to work directly with external clients, explain complex systems, and own outcomes.

Responsibilities

• Embed with client engineering teams on-site and remotely to deliver production-ready Vault implementations.
• Own the technical success of client engagements and translate business requirements into delivery plans.
• Collaborate with client architects, engineers, and program leads to produce technical delivery plans.
• Act as technical point of contact, building trust through consistent delivery.
• Design and deliver scalable, secure, high-availability solutions with internal teams.

Skills

Python

Go

Linux/Unix

Kubernetes

Tools

PostgreSQL

Kafka

Cloud platforms (AWS/GCP/Azure)

Job description

Thought Machine is hiring Forward Deployed Engineers to embed with client engineering teams, delivering production-ready Vault integrations. You will code across backend and tooling, troubleshoot distributed systems, and drive technical success for engagements.

The role requires travel to client sites up to 25% of time, hands-on coding in Python or Go, and strong cloud and Kubernetes experience to ensure robust, scalable deployments for financial-grade environments.