Middle Backend Engineer (Golang, Java, .Net, Scala)

C++ Engineer - Market Data

Ho Chi Minh, Ha Noi - Viet Nam

Product

Maintain/enhance our legacy C++-based tick data processing platform as needed, demonstrating product ownership, and helping migrate datasets to our new tick data processing platform. Contribute to our new, modern C++-based tick data processing platform - enhancing the platform to support additional tick data feeds across asset classes, developing/reviewing the implementation of tick data-based interval features/statistics, and adding new functionality to the platform all while maintaining high software standards and best practices. Collaborate with the Research and Portfolio Management organizations to facilitate the transition from our legacy platform to our new platform, supporting their price volume data needs for signal generation.

Negotiation

AI-Native Software Engineering Lead

Ho Chi Minh - Viet Nam

Outsource

• Backend
• AI

Responsible for developing and evolving the AI-native SDLC operating model, including agent workflow designs, verification gates, context management standards, and evaluation frameworks Build and lead multi-agent systems using orchestration layers such as Claude Code, GitHub Copilot Workspace, Cursor, LangGraph, CrewAI, or equivalent, from prototype to production Collaborate with the Director of Engineering to contribute to and maintain the company's AI toolchain selection criteria and evaluate tools with engineering rigor, providing internal guidance on when AI is beneficial and when it is not Establish engineering standards, agent evaluation loops, and AI output quality gates across the delivery organization Previous experience in a lead, principal, or staff engineer role with demonstrated cross-team influence Experience in outsourcing, consulting, or multi-client delivery environments Track record of building or leading an internal community of practice, guild, or AI adoption program Develop and continuously evolve the company's AI-native SDLC playbook, including standards, workflow templates, case studies, and guardrails that delivery teams can adopt immediately Design and lead internal upskilling programs that transition engineers from AI-assisted to AI-native working patterns Keep track of the AI capability frontier, model improvements, new agent frameworks, and emerging risks, translating signals into timely updates to client practices Work closely alongside Delivery Teams as an AI transformation advisor and execution partner, identifying the highest-value automation opportunities across the SDLC and coordinating with the team to implement them Design and deploy agent-orchestrated workflows tailored to each client's stack, team maturity, and delivery context, with measurable ROI Build business cases for AI-native adoption with clients and account managers, framing the value in terms of velocity, quality, and cost Represent the company's AI-native engineering capabilities in client conversations, QBRs, and RFP responses as a credible technical authority

Negotiation

Platform Lead

Others - Singapore

Product

• Backend
• Devops
• Data Engineering

Develop and expand distributed systems to handle large volumes of sensory, telemetry, and control data across cloud and edge environments, facilitating real-time connections for fleets of robots. Create the API Platform with a focus on high reliability, exceptional developer experience, and robust multimodal AI capabilities accessible through user-friendly APIs and SDKs. Establish extensive training and inference platforms for foundation models used in robot autonomy, teleoperation, and developer integrations. Devise data ingestion and streaming pipelines for real-time connectivity of robot fleets to the cloud, covering various data inputs such as video, LiDAR, joint states, and audio. Oversee and advance a modern cloud native infrastructure stack employing Kubernetes, Docker, and infrastructure as code tools. Ensure platform reliability through telemetry, monitoring, alerting, autoscaling, failover, and disaster recovery measures. Make infrastructure decisions pertaining to distributed storage, consensus protocols, GPU orchestration, network reliability, and API security. Foster collaboration across ML, robotics, and product teams to facilitate hardware in the loop simulation, policy rollout, continuous learning, and CI/CD workflows. Implement secure APIs featuring fine-grained access control, usage metering, rate limiting, and billing integration to accommodate a growing user base.

Negotiation