Product Manager (SaaS)

Senior Technical Project Manager – Automotive Chiplet Software

Ho Chi Minh - Viet Nam

Outsource

Influence international stakeholders, difference domains, difference partners to achieve project goals Taking the responsibility for achieving the project targets and KPIs (on time, on spec, on budget, customer satisfaction). Agreement on project targets with program- and project management, internal difference sub-project teams, external partners (Tier 2 suppliers) and customers (OEMs) and regular reporting of project status Supporting customer acquisitions by preparing customer-specific presentation related software domain in strong cooperation with project, product management and sales (POC) Supporting on setup development process for Tier 2 for ensuring project success business by applying project management tools like quality management, change management, risk management, configuration management as well as defining software development processes setting up PMT for establishing an efficient software development (CD/CI, …) defining an interface adapting customer-specific to ACS project tool chain Furthermore, you will collaborate with both technical and business experts to define work products for the development team and aligning these with OEM and Automotive Chiplet System (ACS) platform requirements support customer demonstration provide technical documentation for the project and ensure its quality according to international standards (e.g. PMI-PMP, VDA, ISO26262, ASPICE, AEC-Q)

Negotiation

Engineering Manager (Data Platform)

Ho Chi Minh - Viet Nam

Offshore

• Data Engineering
• Management

This role focused on data engineering teams with data warehousing, streaming and batch patterns, CI/CD for data pipelines, Drive and coach Agile teams to deliver on engineering standards, sprint backlogs and plans, engineers' responsibilities and performance management, code quality, adherence to development guardrails, and testing; Drive Agile delivery across data platforms, ensuring high standards for; Data quality and testing, Code quality and review practices, CI/CD for data pipelines, Documentation and operational readiness Collaborate closely with data architects, product managers, analytics teams, platform teams, and governance stakeholders to deliver data capabilities aligned with business priorities Own the execution of the data engineering roadmap, balancing short-term delivery with long-term platform sustainability Contribute to data platform architecture and design, including ingestion, transformation, storage, and consumption layers Coach engineers to be T-shaped, capable of working across batch, streaming, analytics engineering, and platform concerns Own and prioritise the remediation of technical and data debt, including legacy pipelines, performance issues, and data quality gaps Stay current with modern data engineering tools, patterns, and methodologies, particularly within the Databricks ecosystem Be accountable for the full lifecycle of data solutions, from design through build, deployment, monitoring, and support Empower teams to be self-sufficient, disciplined, and accountable for the reliability of data products Lead initiatives to improve data delivery processes, including automation, observability, and operational excellence Motivate teams to continuously improve through innovation, experimentation, and continuous delivery Drive career development and progression for data engineers, partnering with HR on performance management and growth paths

Negotiation

Product Manager (Data & Models)

Ho Chi Minh - Viet Nam

Product

• Product Management
• AI

Designing data strategy and model integration for creating efficient data pipelines, evaluation frameworks, and annotation systems to maintain high-performance LLMs. Responsible for ensuring data quality standards and implementing bias mitigation and privacy-preserving techniques. Defining the product's core model roadmaps, taking into account technical feasibility, user needs, and ethical considerations. Collaboration with researchers to incorporate experimental breakthroughs into deployable features. Partnering with Engineering and Research teams to ensure model development aligns with product goals and advocating for transparency in model decision-making to build user trust. Analyzing usage patterns from open-source communities (Discord, Reddit, GitHub) to refine model behavior and address real-world edge cases, contributing to community-driven model evolution. Setting performance benchmarks, cost efficiency, and resource utilization standards for model scalability and reliability.

Negotiation