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
