Applied Machine Learning/AI Software Engineer

Key Roles & Responsibilities

• Innovate and Deploy: Bridge the gap between machine learning (ML) / AI model development and real-world software applications, possessing both ML expertise and full-stack development skills, to work from ideation all the way to deployment.


• Optimise and Scale:Leverage on existing latest ML frameworks and AI models, to create optimized, maintainable and scalable code that can be deployed as/or into a product.


• Collaborate: Work closely with business stakeholders, software and data engineers, product leads/managers to understand complex business challenges and deliver AI-powered solutions


• Quality code production:Write high-quality, well-tested code following best practices and coding standards

Qualifications, Skills & Experience

• Bachelor's/Master's degree in Computer Science, Machine Learning, Data Science, or a related field.


• 3+ years of non-internship professional software development experience.


• Practical experience in at least one of the following domains: time series forecasting, anomaly detection, search and recommendation systems, feedback control, interpretable machine learning or computer vision.

Applied MachineLearning (ML) Skills:

• Proficiency in frameworks like PyTorch or Tensorflow


• Strong foundation in data structures, algorithms, and software engineering principles.


• Experience with LLMs and emerging area of prompt-engineering.


• Experience deploying ML workloads on Microsoft Azure, Huawei or similar cloud platforms.


• Good to have experience with agents framework such as Langchain, vector DBs.


• Familiarity with Azure ML, MLflow, or similar MLOps platforms.

Software Engineering+ Cloud Skills:

• Proficiency in Python; experience with NodeJS is a strong plus.


• Familiarity with frontend integration workflows (Angular/React, REST APIs). Frontend coding experience with


• Angular/React Framework is a plus.


• Understanding of containerization and orchestration (Docker, Kubernetes/AKS).

Soft Skills:

• Demonstrated experience in requirement analysis, can transform business problems into ML solutions very


• well, can communicate with bothtechnical and non-technical stakeholders clearly


• Strong communication skills with an ability to explain concepts in simple terms to technical and non-technical audiences