AI Engineer

Tsai Center for Innovative Thinking at Yale (Tsai CITY)

AI Engineer building and operating the AI systems behind Genie for enterprise teams. Focusing on transforming fragmented data into reliable intelligence and maintaining system performance.

Posted 7/5/2026full-timeHo Chi Minh City • 🇻🇳 VietnamMid-LevelSeniorWebsite

Tech Stack

Tools & technologies

AWSCloudGoogle Cloud PlatformPythonSQL

About the role

Key responsibilities & impact

Design, build, test and operate AI services and workflows from proof of concept through production deployment.
Develop capabilities across classification, extraction, summarisation, entity resolution, stakeholder mapping, retrieval, risk detection and insight generation.
Select the appropriate combination of deterministic software, machine learning, retrieval, LLMs, agents and human review for each use case.
Build clear APIs and integrations that connect AI capabilities to Genie and related internal systems.
Own the quality, reliability and maintainability of the components you ship, including production support and continuous improvement.
Build and improve pipelines for ingestion, cleaning, enrichment, labelling, chunking, indexing and retrieval.
Protect sensitive and customer-specific data through secure handling, access controls, workspace boundaries and careful use of external model providers.
Identify and mitigate risks including prompt injection, data leakage, stale context, unsupported claims, excessive autonomy and unsafe tool permissions.
Translate ambiguous customer and business workflows into clear technical requirements, experiments and acceptance criteria.
Communicate technical trade-offs, limitations and evidence clearly to both technical and non-technical colleagues.
Maintain clear technical documentation, implementation notes and decision records in shared systems such as Jira and Confluence.
Participate actively in code reviews, design reviews, sprint planning, incident learning and architectural discussions.

Requirements

What you’ll need

Typically 4+ years of experience in software, data, machine learning or AI engineering, with evidence of shipping and operating production systems.
Professional proficiency in Python and practical experience building tested, maintainable services or data workflows.
Strong SQL capability is expected.
Hands-on experience with LLM applications, retrieval-augmented generation, embeddings, structured extraction, orchestration and evaluation.
Experience with data pipelines, APIs, version control, automated testing, CI/CD and cloud environments such as GCP or AWS.
Working knowledge of production observability, including logging, tracing, latency, failures, model usage and cost monitoring.
Sound judgement around sensitive data, model limitations, prompt injection, tool permissions, human review and rollback.
Professional English communication skills, particularly for technical documentation, code review and cross-border architectural discussion.
A bachelor's degree in computer science, engineering or a related field, or equivalent practical experience.

Benefits

Comp & perks

Competitive local package
Equity options
13th-month bonus

ATS Keywords

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Hard Skills & Tools

Machine LearningData Workflow DevelopmentAPI IntegrationAutomated TestingCI/CDData IngestionEntity ResolutionRisk DetectionTechnical DocumentationProduction Support

Soft Skills

Professional English CommunicationCross-Functional CollaborationJudgment Around Sensitive Data

Certifications

Bachelor's Degree in Computer ScienceEngineering or Related Field