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Singapore
Hybrid
Job Description Summary
For over forty years, HarbourVest has been home to a committed team of professionals with an entrepreneurial spirit and a desire to deliver impactful solutions to our clients and investing partners. As our global firm grows, we continue to add individuals who seek a collaborative, open-door culture that values diversity and innovative thinking.
In our collegial environment that’s marked by low turnover and high energy, you’ll be inspired to grow and thrive. Here, you will be encouraged to build on your strengths and acquire new skills and experiences.
We are committed to fostering an environment of inclusion that promotes mutual respect among all employees. Understanding and valuing these differences optimizes the potential of both the individual and the firm.
HarbourVest is an equal opportunity employer.
This role follows a hybrid work model which consists of 4 days in office per week. Additionally, you will receive 5 additional remote days per quarter to use at your discretion, subject to manager approval.
Join us as an Artificial Intelligence Engineer at HarbourVest!
In this role, you will help scale Data Science and AI delivery in APAC by focusing on the engineering, deployment, and productionization of analytics and AI solutions. You will work closely with the APAC Data Science Team and business stakeholders to turn prototypes, dashboards, and analytical ideas into reliable, supportable, and enterprise-aligned products.
This position involves working with minimal direction, combining strong engineering fundamentals with a pragmatic approach to analytics delivery. You will play a key role in strengthening APAC’s regional capability while aligning with global Data Science, Analytics, and IT standards.
The ideal candidate is someone who:
Has strong engineering foundations and enjoys building reliable, production-grade analytics solutions.
Is comfortable working across analytics engineering, AI engineering, and light DevOps responsibilities.
Understands the principles of analytics development and applies software engineering guidelines (modularity, testing, version control) to data workflows.
Demonstrates a problem-solving approach with strong attention to detail, organization, and documentation.
Thrives in a collaborative, inclusive, and professional environment.
Takes a practical, business-focused approach to analytics and AI delivery.
Is intellectually curious, cognitively flexible and motivated by continuous learning and evolving technologies, particularly in AI-enabled analytics.
What you will do:
Productionize Data Science and AI prototypes into reliable, supportable applications and services, including deployment of Python-based analytics solutions using containerized or cloud-based delivery patterns.
Enable business users and citizen developers by providing technical guidance, templates, and safe pathways from prototype to production.
Partner with global Data Science and engineering teams to apply clean architecture, source control, testing, and deployment standards.
Build and maintain clear user documentation, runbooks, and knowledge resources to ensure sustainment and continuity.
Collaborate closely with the Data Science team to reduce operational support burden and amplify advanced analytics and AI impact.
Provide APAC time-zone support for analytics engineering, deployment, solving, and operational handoff.
Support and modernize legacy analytics solutions, transitioning them into scalable, maintainable patterns using Python, Snowflake, and governed platforms.
Maintain and enhance existing analytics products, including Power BI dashboards and recurring analytical workflows.
And other responsibilities as required
What you bring:
Solid experience in analytics engineering, software development, or AI-related engineering roles.
Practical experience with Python and data-focused application development.
Familiarity with analytics platforms such as Power BI, Snowflake, or similar data ecosystems.
Experience with cloud-based deployment concepts, DevOps practices, version control systems (e.g. Git), and/or CI/CD pipelines is strongly preferred.
Strong analytical thinking and problem-solving capabilities.
Clear communicator who works effectively with both technical and non-technical collaborators.
Accepting diversity and working hand in hand, we thrive collectively.
Methodical and structured approach to delivering high-quality, well-documented work.
Education Preferred:
Bachelor’s or Master’s degree or experience in Computer Science, Data Science, Engineering, or a related field, or practical experience.
Experience:
2–5 years of experience in analytics engineering, software engineering, data engineering, or a related technical role preferred.