OVERVIEW:

We are seeking a driven and innovative Machine Learning & AI Engineer to join our team
and help shape the future of intelligent systems. In this role, you’ll work closely with
multidisciplinary teams to create and deploy machine learning solutions that directly
impact our products and business operations. From building predictive analytics to
integrating large-scale models into production environments, you’ll be part of a team that
leverages cutting-edge AI to solve real-world challenges.
If you’re excited about applying artificial intelligence to meaningful problems and thrive in a
collaborative, fast-paced environment, we’d love to hear from you. Come be a part of our
mission to transform business with intelligent technology.

QUALIFICATIONS:

• Education: Bachelor’s or Master’s in Computer Science, Machine Learning, Data Science, or a closely related field. Equivalent practical experience will also be considered.
• Experience: Minimum 2 years of hands-on experience developing and deploying machine learning solutions in a professional setting.

TECHNICAL SKILLS:

• Proficiency in Python and common data science libraries (NumPy, pandas, etc.)
• Deep understanding of ML frameworks such as scikit-learn, TensorFlow, PyTorch
• Experience with model training, tuning, and performance evaluation
• Solid grasp of statistics, probability, and experimental design
• Familiarity with cloud platforms like AWS, GCP, or Azure for ML workflows
• Bonus: Experience with Large Language Models (LLMs), Generative AI, or MLOps pipelines

SOFT SKILLS:

• Strong problem-solving skills and a methodical approach to building intelligent solutions
• Ability to communicate complex technical topics clearly to stakeholders across technical and non-technical backgrounds
• Team player with a proactive mindset and eagerness to continuously learn and evolve
  Your recruiter for this job:
  WhatsApp Jammy @ 6421 4954 for a quicker response.
  Connect with me on
  Jammy | jam@talentsis.com.sg
  Talentsis Pte Ltd | EA No: 20C031

• Build intelligent systems: Design, develop, and refine machine learning models tailored to solve specific organizational challenges, using both structured and unstructured data.
• End-to-end ML lifecycle ownership: Own the entire model lifecycle from data preprocessing, feature engineering, and model training to validation, deployment, and monitoring.
• Model integration and deployment: Work with engineering and DevOps teams to deploy models via APIs, containers, or cloud-native tools—ensuring scalable and reliable production deployments.
• Evaluate and iterate: Use experimentation frameworks and validation techniques such as A/B testing, cross-validation, and performance tuning to enhance model robustness and accuracy.
• Data exploration and insight discovery: Conduct in-depth exploratory data analysis and translate findings into actionable insights through visualization and storytelling.
• Stay current: Regularly explore academic and industry developments in AI/ML, including deep learning, LLMs, and generative AI, and recommend ways to incorporate them into existing workflows and solutions.