Machine Learning Engineer

Snappymob; Kuala Lumpur; On-site; MYR 90,000 - 120,000; Full time

Job summary

An innovative tech company in Malaysia is seeking a Machine Learning Engineer to design and implement ML models for financial applications. The role requires strong proficiency in Python, knowledge of financial markets, and experience with various ML frameworks. This position offers the opportunity to work on exciting projects with a focus on career growth, a supportive work environment, and flexible work arrangements including remote work days.

Benefits

Employer-Sponsored Work Visa

Mentorship and continuous learning opportunities

Flexible work options

Convenient location near public transport

Qualifications

• Ability to translate complex financial and business challenges into ML-driven solutions.
• Solid understanding of financial markets and algorithmic trading.
• Experience in fine-tuning generative AI models.

Responsibilities

• Design, prototype, and implement ML models for financial applications.
• Collect, clean, and preprocess financial data from multiple sources.
• Work with teams to deploy and scale ML models in real-world environments.

Skills

Machine Learning

Python

Data Scraping

Financial Markets Knowledge

Risk Management

Education

Degree in Computer Science or related field

Tools

PyTorch

TensorFlow

Scikit-learn

Git

Docker

Kubernetes

Job description

About Snappymob Malaysia
Snappymob builds top-tier custom iOS, Android, and web applications that deliver seamless user experiences and scalability. From startups to enterprises, we turn ideas into functional, user-friendly solutions. Check us out at snappymob.com !

Key Responsibilities

Design, prototype, and implement ML models for financial applications, including predictive analytics, risk modeling, and trading algorithms.

Identify, collect, clean, and preprocess structured and unstructured financial data from multiple sources, including web data extraction.

Build and optimize training pipelines for experimentation and production deployment. Work with engineers to deploy and scale ML models in real-world environments.

Fine-tune and optimize foundational AI models, including LLMs, RAG, and agent-based applications to enhance decision-making and automation.

Work closely with cross-functional teams, including software engineers, data scientists, and business stakeholders, to translate business needs into effective ML solutions.

Define the data science roadmap and establish best practices for model development, versioning, and deployment. Stay updated on industry trends and emerging technologies.

What would you need?

• Ability to translate complex financial and business challenges into ML-driven solutions and communicate findings effectively.
• Solid understanding of financial markets, algorithmic trading, and risk management strategies.
• Experience in classical ML models, inference systems, and fine-tuning generative AI models.
• Strong skills in data scraping, extraction, transformation, and pipeline development for large-scale datasets.
• Proficiency in Python and ML frameworks like PyTorch, TensorFlow, Keras, Scikit-learn, NumPy, and Pandas.
• Strong coding ability with experience in Git, CI/CD, Docker, and Kubernetes.
• Familiarity with MLflow, Kubeflow, Airflow, and cloud-based ML services for model management and deployment.
• Shown a track record of being able to execute with minimal high-level guidance from C-levels.

Nice to have:

Experience with Spark, Dask, Snowflake, and BigQuery for large-scale data handling.

Knowledge of Graph Neural Networks (GNNs) and time-series forecasting for financial applications.

Experience with LLMs, RAG, LangChain, LlamaIndex, and AI-driven automation.

Familiarity with AWS, GCP, or Azure for scalable ML model deployment.

Opportunity to shape a new business: Ever wonder what it takes to launch a start-up? Heres your chance.

Employer-Sponsored Work Visa: Backing your relocation with full visa sponsorship.

Innovative Projects: Work on exciting projects that push your skills further.

Accessible Location: Conveniently located near the LRT for easy commuting.

Career Growth: Mentorship, and continuous learning.

Flexible Work: Enjoy a better work-life balance with flexible options: 2 days WFH, 3 days in-office, and flexible working hours.