Machine Learning

Key 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

Requirements:

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 Machine Learning (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 both technical and non-technical stakeholders clearly

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