Data & Machine Learning Engineer

**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**:

- 3 - 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**,