Total 2 process engineer job vacancies

About the Role
We are looking for a Junior to Mid-Level ML Engineer to join the Model Engineering team. This role is suited for someone who enjoys working hands-on with computer vision models and understands models as systems to be trained, evaluated, improved, and optimised. You will work across the model lifecycle, including dataset preparation, architecture development, model training, evaluation, performance analysis, and optimisation for real-world computer vision applications.

Responsibilities

• Train, fine-tune, and evaluate computer vision models for classification, object detection, segmentation, pose estimation, and action recognition.
• Improve model performance through architecture enhancements, custom heads, backbone modifications, loss functions, and training strategy optimisation.
• Design experiments, conduct ablation studies, benchmark new techniques, and analyse model trade-offs.
• Investigate edge cases, debug failure cases, and improve model accuracy, robustness, and efficiency.
• Optimise models based on practical constraints such as latency, throughput, resource usage, and hardware limitations.
• Collaborate with engineering teams to improve dataset quality, model performance, and system reliability.

Requirements

• Bachelor’s degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, or a related technical field.
• At least 2 years of hands-on experience training, fine-tuning, and evaluating computer vision models.
• Strong hands-on experience with PyTorch and/or TensorFlow.
• Proficiency in Python and familiarity with Linux environments and command-line tools.
• Experience with computer vision tasks such as classification, object detection, segmentation, pose estimation, or action recognition.
• Experience modifying models beyond configuration-level changes, including architectures, backbones, heads, loss functions, or training pipelines.
• Experience designing experiments, conducting ablation studies, analysing model performance, and debugging failure cases.
• Familiarity with multimodal, vision-language, foundation, or open-vocabulary models.
• Experience with model optimisation techniques such as quantisation, pruning, or distillation.
• Familiarity with experiment tracking, reproducibility, and structured machine learning workflows.

Dear Applicant,

If you or someone you know isinterested, please send the CV directly to VeronicaE@evolutionjobs.sg (mostpreferred, as I may overlook some CVs due to the high volume).

Please note that visasponsorship is not available at this time.

JD

AI Solutions Engineer – 1-Year Renewable Contract
Industry: Insurance
Location: Singapore
Contract: 1-year renewable

We are looking for an AI Solutions Engineer tosupport AI/GenAI initiatives within the insurance domain. The ideal candidateshould have strong hands-on experience in AWS / Azure, with the abilityto design, build, and deploy AI solutions in an enterprise environment.

Key Responsibilities:

Design, develop, and implement AI/GenAI solutions using Amazonand Microsoft.

Work with business and technology stakeholders to understandrequirements and translate them into scalable AI solutions.

Build and integrate AI models, APIs, workflows, andautomation tools into existing systems.

Support proof-of-concepts, solution design, testing,deployment, and production readiness.

Collaborate with data, cloud, security, and applicationteams to ensure solutions meet enterprise standards.

Provide technical guidance on AI architecture, cloudintegration, and best practices.

Requirements:

Strong experience as an AI Engineer / AI SolutionsEngineer / Cloud AI Engineer / Machine Learning Engineer.

Must have hands-on experience with AWS/Azure

Good understanding of GenAI, LLMs, prompt engineering, RAG,model integration, and AI solution deployment.

Experience with Python, APIs, cloud-native architecture, andenterprise application integration.

Insurance or financial services experience is highlypreferred.

Strong communication skills and ability to work with bothtechnical and business stakeholders.