Data & Machine Learning Engineer

Jobtech Pte. Ltd.; Singapore; On-site; SGD 80,000 - 120,000; Full time

Job summary

A leading technology company in Singapore is seeking a skilled Data & Machine Learning Engineer to enhance their AI-powered products. You will be responsible for building data pipelines, optimizing ML workflows, and collaborating on human-AI integration. Ideal candidates will have extensive experience in data engineering and a solid understanding of machine learning principles, particularly as they relate to natural language processing and automation. This role promises ample opportunities for impactful work in a dynamic environment.

Qualifications

• At least 5 years of experience in data engineering, ML engineering, or applied data science roles.
• Experience with unstructured textual data and NLP systems preferred.
• Proven ability to build and maintain data pipelines.

Responsibilities

• Design, build, and maintain ETL/ELT pipelines for analytics and ML workflows.
• Implement and maintain pipelines for ML models.
• Collaborate with academic partners to develop data models and frameworks.

Skills

Data Engineering

Machine Learning Engineering

Data Analysis

Natural Language Processing (NLP)

Python

SQL

NoSQL Databases

Cloud Platforms

Tools

AWS

GCP

Azure

Next.js

Job description

Role Summary

As a Data & Machine Learning Engineer, you'll bridge the gap between raw data, machine learning systems, and actionable insights. You will build and maintain data pipelines, refine ML workflows, and drive the production-readiness of our AI-powered products.

You'll also play a key role in integrating Human+AI collaboration into day-to-day workflows - using AI agents to enhance productivity, automate repetitive tasks, and unlock scalable intelligence delivery.

This role supports both internal teams and external partners through data reliability, smart automation, and insight-ready systems; with the long-term goal of enabling agentic AI systems that can reason, act, and adapt autonomously to deliver business value at scale.

Key Responsibilities

Data, Product & Research (70%)

Data Engineering & Infrastructure

• Design, build, and maintain ETL/ELT pipelines to support analytics and machine learning workflows.
• Manage and optimise data infrastructure across cloud platforms (AWS, GCP, Azure) and on-premise servers.
• Ensure data integrity, reliability, and governance across internal and partner-facing datasets.
• Monitor and improve performance of production data systems and ingestion pipelines.
• Develop and maintain JobTech's labour demand data acquisition pipelines and framework, ensuring high-quality, timely, and scalable data collection across job portals and other sources.

Machine Learning Engineering & MLOps

• Implement and maintain pipelines to refresh ML models automatically with new data.
• Transition legacy rule-based models to machine learning driven systems for skill extraction and clustering.
• Develop workflows for model deployment, versioning, monitoring, and scaling (MLOps readiness).
• Support model evaluation through automated testing, A/B experiments, and performance tracking.

Data Infrastructure & Quality

• Build and maintain data ingestion and transformation workflows, ensuring data is processed, documented, and delivered accurately across internal systems and use cases.
• Maintain high-quality pipelines for (JobTech Labour Market Information (LMI), partner uploads, campaign metrics) and integrity.

Research Collaboration & IP Development

• Collaborate with academic and research partners to co-develop data models, insight frameworks, and new intellectual property (IP) that can power future JobTech products or publications.

Customer (20%)

Insights, Reporting & Customer Enablement

• Work closely with Sales, Marketing, and Customer Success teams to generate insight decks, campaign data summaries, and customer-facing reports.
• Translate unstructured textual data into structured insights (e.g., job descriptions, CVs, skills frameworks) and manage JobTech's public analytics dashboards.
• Run A/B tests on platform usage or behavioural data to inform product or UX decisions.
• Address internal and client queries related to JobTech's data, analytics, and model outputs.

People & AI - Hybrid Collaboration (10%)

Cross-functional Support

• Collaborate with implementation teams to deliver integrated data solutions for customer campaigns.
• Participate in end-to-end project cycles including requirements gathering, validation, UAT, and production cutover.
• Work with engineering and product teams to embed AI and insights into platform features and workflows

AI Agents & Collaboration

• Use AI agents to accelerate coding, documentation, and data validation workflows
• Collaborate with team members to embed prompt engineering into analytics, insight generation, and automation tasks
• Help shape best practices for day-to-day AI tool usage to increase team productivity and scale high-impact work

Requirements

Experience

• At least 5 years of hands-on experience in data engineering, ML engineering, or applied data science roles in fast-paced environments.
• Experience working with unstructured textual data and NLP-based systems is preferred.
• Proven ability to build, maintain, and optimise data pipelines and analytical workflows.

Skills

• Proficient in Python for data analytics and pipeline development.
• Solid knowledge of SQL and NoSQL databases, with experience in both relational (e.g. MariaDB, Postgres), cloud-based data warehouses (e.g. BigQuery, Redshift), and document-based systems.
• Familiar with text mining and natural language processing (NLP) techniques; hands-on implementation experience is a plus.
• Experienced in using AI agents (e.g. ChatGPT, Claude) to augment daily development tasks such as code generation, testing, documentation, and insight drafting.
• Comfortable working with interactive visualisation libraries or frameworks (e.g. Plotly, Dash, D3.js); experience building dashboards is an advantage.
• Programming experience in Next.js is a bonus, especially for embedding insights into product UI.
• Working knowledge of microservices architecture, cloud infrastructure platforms (AWS, GCP, Azure), and CI/CD deployment processes.
• Strong documentation and code review habits for collaborative development and production readiness.

Soft Skills

• Self-starter with a proactive mindset and the ability to work independently and in cross-functional teams.
• Strong analytical thinking, with an eye for detail and a pragmatic approach to solving real-world data problems.
• Effective communicator who can explain technical work clearly to non-technical stakeholders.
• Curious, adaptable, and committed to building resilient, production-grade solutions.

Information Security & Compliance Responsibilities

• Policy Adherence: Follow JobTech's information security policies, procedures, and best practices consistently.
• Risk Management: Identify, assess, and mitigate security risks to safeguard sensitive and confidential information.
• Incident Response: Support the detection, reporting, analysis, and response to security incidents or breaches.
• Security Awareness: Participate in regular information security training and uphold best practices across all activities.
• Data Protection: Ensure the confidentiality, integrity, and availability of JobTech data and information assets.
• Regulatory and Legal Compliance: Adhere to all relevant legal, regulatory, and contractual obligations related to information security.
• Team Collaboration: Work closely with internal cross-functional teams to ensure integrated and consistent security measures.
• Confidentiality and Data Handling: Protect sensitive information and maintain strict confidentiality in line with JobTech's data protection standards.
• Continuous Improvement: Stay current with evolving security threats and technologies, and contribute to strengthening JobTech's overall security posture.

Bonus Traits

• Experience with skills taxonomies, embeddings, or large language models.
• Familiarity with SOC2, ISO 27001, or data security principles.
• Prior experience deploying ML systems in real-time or near-real-time settings.
• Contributor to internal AI knowledge bases or MLOps frameworks.
• Experience collaborating with researchers, universities, or think tanks to produce applied data research or co-develop commercialisable IP.
• Interest in designing toward agentic AI systems that combine data, reasoning, and task orchestration for autonomous value delivery.