Enterprise Architect - AI Presales role, having an experience between 3-8 years. The focus is a combination of technical knowledge, business understanding, solution design, and customer engagement.
Enterprise Architect - AI Presales role, having an experience between 3-8 years. The focus is a combination of technical knowledge, business understanding, solution design, and customer engagement.
Senior engineer responsible for building and maintaining tools that analyze and debug autonomous vehicle data, leading multi‑project workstreams and driving adoption of new features.
Compensation
The salary range for this role: is $144,700 to $261,300.
Responsibilities
Build and maintain data lifecycle tools for AV data
Design and implement search and retrieval systems using SQL and vector databases
Develop high‑performance replay tools for sensor data
Lead technical design and code reviews
Mentor engineers and foster AI‑assisted development
Requirements
Bachelor's degree in Computer Science, Engineering, or a related field
A client-embedded technical leader responsible for translating AI ambition into trusted, enterprise-grade solutions, blending deep hands-on engineering, consulting judgment, and client-facing delivery ownership.
Compensation
We offer a competitive remuneration package.
Responsibilities
Turn ambiguous business problems into concrete AI solutions by partnering with clients to understand workflows, constraints, success metrics, and incentives.
Translate business challenges into well-scoped AI use cases (e.g., prediction, automation, retrieval, agent workflows).
Define MVP scope, data requirements, and evaluation criteria jointly with stakeholders.
Build fast prototypes using - LLMs, RAG pipelines, fine-tuning, agents, traditional ML models where appropriate.
Design and implement AI architectures, including data ingestion, feature pipelines, embeddings, vector stores, model selection, tuning, and evaluation.
Enterprise Architect - AI Presales role, having an experience between 3-8 years. The focus is a combination of technical knowledge, business understanding, solution design, and customer engagement.
Enterprise Architect - AI Presales role, having an experience between 3-8 years. The focus is a combination of technical knowledge, business understanding, solution design, and customer engagement.
As a Fullstack Developer, you will play a key role in the design, development, and maintenance of comprehensive web applications for the Corporate and Investment Banking (CIB).
Responsibilities
Designing and implementing solutions from the backend to the frontend for Corporate and Investment Banking (CIB)
Contributing to the design of robust, scalable architectures, potentially integrating AI/ML components
Developing and maintaining APIs to ensure smooth communication between services
Designing database schemas, optimizing queries, and efficiently managing relational, NoSQL, and vector databases
Participating in CI/CD pipelines for application deployment, monitoring, and maintenance, as well as the industrialization of AI/Data solutions
Requirements
University degree (Bachelor's or Master's equivalent) in Computer Science, Software Engineering, or a related field
Enterprise Architect - AI Presales role, having an experience between 3-8 years. The focus is a combination of technical knowledge, business understanding, solution design, and customer engagement.
Senior Python Engineer responsible for designing, building, and scaling backend systems that power AI across the platform.
Locations (2)
Hanoi, Hanoi, Vietnam
Ho Chi Minh City, Ho Chi Minh City (HCMC), Vietnam
Responsibilities
Design, implement, and maintain robust Python/FastAPI microservices and backend systems that integrate LLMs and generative AI capabilities directly into live workflows.
Build automated pipelines and tooling to continuously measure, evaluate, and improve LLM output accuracy, consistency, latency, and safety.
Develop internal libraries, abstractions, and standardized patterns that empower engineering squads to ship AI features quickly and reliably.
Optimize AI services and inference pipelines for throughput, latency, token economics, caching, and high availability.
Partner with the CTO and technical leads to evaluate emerging AI technologies, architect retrieval-augmented generation (RAG) pipelines, and design vector search infrastructure.
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