Lead Software Architect
JOB DESCRIPTION
JOB REQUIREMENT
WHAT'S ON OFFER
CONTACT
Job Summary
Company Type:
Product, AI Application Platform
Technical Skills:
Python
Location:
Ho Chi Minh - Viet Nam
Working Policy:
Salary:
Negotiation
Job ID:
J01204
Status:
Close
Related Job:
Technical Leader
Ho Chi Minh - Viet Nam
Product
- Python
We are looking for a product-minded Technical Lead who possesses a strong engineering foundation and the leadership capability to drive our backend and AI initiatives. You will not only architect scalable Python systems but also align technical decisions with business goals. In this role, you are expected to own the product lifecycle end-to-end-from design to operation-while building a high-trust, high-performance engineering culture. You must be adaptable, ready to lead the team through technology shifts (specifically in AI integration), and capable of balancing speed, quality, and cost based on the product phase.#What You'll Do Product-Centric Engineering & Strategy Product Mindset: Work closely with Product Managers to understand user pain points and value features. Make technical trade-offs based on the current product phase (e.g., MVP vs. scaling). Ownership: Take end-to-end responsibility for features: Design → Development → Release → Operation. Proactively propose solutions and identify risks before they become issues. Adaptability: Lead the team in adapting to new technology directions, particularly integrating AI/ML workflows into the backend. Be willing to pivot technical approaches when product direction changes. Architecture & Technical Foundation System Design: Architect robust systems with a clear understanding of when to use Monolith vs. Microservices. Design efficient data models, data flows, and versioned APIs. Cloud & Infrastructure: Leverage AWS services effectively. Assess the risks and benefits of integrating external services versus building in-house. Performance & Security: Ensure systems are designed for scalability, high performance, and security while keeping infrastructure costs optimized. Delivery & Execution Execution: Ensure on-time releases with the required quality standards. Manage scope creep and handle cross-team/external dependencies effectively. Risk Management: Provide honest reporting to management. Do not hide risks; instead, communicate them early with mitigation plans. Operational Excellence: Maintain system stability and reliability in production. Leadership & Mentorship Team Building: Build a strong engineering culture and standardize coding practices. protect the team from distractions while ensuring members trust and follow your technical direction. Mentorship: Conduct code reviews to mentor the team on mindset and standards (SOLID, DRY). Delegate tasks effectively-assigning the right people to the right jobs. Communication: Act as a bridge between technical and non-technical stakeholders. Explain technical decisions to the CEO and Product teams using business language (Cost, Risk, Impact). Align expectations on scope and delivery explicitly from the start.
Negotiation
View detailsAI-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
View detailsPlatform 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.