AI Engineering Lead

prudential services singapore pte. ltd.; Singapore; On-site; SGD 120,000 - 180,000; Full time

Job summary

Prudential Services Singapore Pte. Ltd. is seeking an AI Engineer to join the DPS Engineering team. You will design, build, and deploy AI-driven capabilities that enhance product features, improve operational efficiency, and enable data-driven decisions.

You will work with product managers, engineers, and domain experts to translate requirements into scalable AI solutions, ensuring smooth integration into production systems and adherence to governance standards.

Qualifications

• Proven experience delivering AI/ML solutions in production environments.
• Strong Python and ML framework skills with practical GenAI work.
• Familiarity with CI/CD and MLOps practices.

Responsibilities

• Design, build, and deploy AI models aligned with product goals.
• Collaborate with product managers and engineers for production-ready features.
• Maintain ML pipelines from data ingestion to deployment and monitoring.
• Ensure governance, security, and responsible AI practices.
• Translate business problems into measurable AI solutions.

Skills

Python

AI/ML frameworks

GenAI / LLMs

MLOps

Cloud platforms

Data engineering concepts

Education

Bachelor’s degree or higher in a relevant field

Tools

TensorFlow

PyTorch

Scikit-learn

Job description

We are seeking an AI Engineer to join the DPS Engineering team, embedded within a cross-functional product delivery squad. This role will focus on designing, building, and deploying AI-driven capabilities that enhance product features, improve operational efficiency, and support data-driven decision-making.

The individual will work closely with product managers, engineers, and domain experts to translate business requirements into scalable AI solutions, ensuring seamless integration into production systems.

1. AI Solution Development

• Design, develop, and deploy machine learning and AI models (e.g., NLP, predictive analytics, GenAI use cases) aligned with product objectives
• Translate business problems into AI/ML solutions with clear success metrics

2. Product Integration

• Embed AI capabilities into product features and workflows within the delivery squad
• Collaborate with backend/frontend engineers to ensure scalable and reliable deployment

3. Model Lifecycle Management

• Build and maintain end-to-end ML pipelines (data ingestion, training, evaluation, deployment, monitoring)
• Ensure continuous improvement through model retraining and performance tuning

4. Data & Engineering Collaboration

• Work with data engineers to define data requirements, pipelines, and data quality standards
• Ensure proper feature engineering and dataset governance

5. Risk, Governance & Responsible AI

• Ensure AI solutions comply with enterprise standards on security, privacy, and responsible AI usage
• Document models, assumptions, and limitations clearly

Requirements:

• Strong experience in Python and AI/ML frameworks (e.g., TensorFlow, PyTorch, Scikit-learn)
• Experience with GenAI / LLMs (e.g., prompt engineering, RAG architectures, API-based models)
• Familiarity with MLOps practices (CI/CD, model deployment, monitoring)
• Experience working with cloud platforms (Azure preferred, AWS/GCP acceptable)
• Knowledge of data engineering concepts (SQL, data pipelines, APIs)
• Proven track record of delivering AI solutions in production environments
• Experience working in agile, squad-based delivery models
• Strong problem-solving mindset with a focus on business impact
• Ability to work in fast-paced, iterative delivery environments
• Effective collaboration across engineering, product, and business teams
• Curiosity and drive to continuously learn emerging AI technologies