AI Engineer

Company Introduction:

At SNSoft, we're at the forefront of technological advancement, integrating cutting-edge solutions into everyday life. Our culture thrives on innovation, collaboration, and a commitment to excellence. We foster an inclusive environment where every team member's contribution is valued and where everyone has the opportunity to grow.

Benefits and Compensation:

• Competitive salary range depending on experience.
• Project Incentive (upon Company Declaration based on project performance)
• Transport, Meal, Birthday Allowances (upon Confirmation)
• Sports Activities (Badminton & Basketball), Company Outing
• Training and certification sponsored by company (Selected employees).
• Optical, Dental, Body Check Up Claim (upon Confirmation)

Job Summary:

We're a 35+ person, fully in-house Data & AI team powering business operations across global markets — and we're continuing to grow rapidly. As a Machine Learning Engineer, you'll design, build, and deploy production-ready machine learning solutions that solve complex business problems and improve our products and services. You'll work closely with data scientists, software engineers, and domain experts to turn data and AI capabilities into scalable, real-world applications, including AI-enhanced customer service. Our team works across a wide range of production AI and data systems, including full-funnel ad attribution across Meta, TikTok, Google, X, and Bing with an automated rules engine; churn and retention modeling for precision marketing; a configurable LLM-powered customer service platform; and an agent-driven risk control platform combining stream processing with graph databases.You'll have the opportunity to work on systems that go beyond model experimentation — from model development and deployment to real-time decisioning, LLM applications, automation, and large-scale production AI.

Key Responsibilities:

• Model Development: Design, build, and maintain scalable machine learning models and algorithms.
• Data Analysis: Analyze and preprocess data from various sources to prepare it for model training.
• Model Training & Evaluation: Train, validate, and tune machine learning models to achieve optimal performance.
• Deployment: Deploy models into production and integrate them with existing systems.
• Monitoring & Maintenance: Monitor model performance in production and update models as necessary to ensure they remain accurate and relevant.
• Collaboration: Work with cross-functional teams, including data scientists, software engineers, and product managers, to understand requirements and deliver solutions.
• Research: Stay current with the latest developments in machine learning and AI to continuously improve our technology stack.

Required Qualifications:

• Education: Bachelor’s or Master’s degree in Computer Science, Engineering, Mathematics, Statistics, or a related field.
• Experience: Demonstrated at least 3 years experience as a Machine Learning Engineer or in a similar role.
• Programming Skills: Expertise in programming languages such as Python, golang, or Java.
• Machine Learning Frameworks: Proficient with machine learning frameworks and libraries like TensorFlow, PyTorch, JAX, scikit-learn, or equivalent.
• Data Management: Strong understanding of data management and processing tools such as SQL, Hadoop, Spark, etc.
• Problem-Solving: Exceptional analytical and problem-solving abilities.
• Data Analysis Tools: Skilled in using data analysis and visualization tools like NumPy, Pandas, and Matplotlib.
• NLP Experience: Familiarity with natural language processing concepts and large language models, including transformers and attention mechanisms.

Preferred Qualifications:

• Advanced Education: Ph.D. in a relevant field.
• Domain Knowledge: Experience in specific domains such as natural language processing (NLP), recommendation systems, computer vision, etc.
• Big Data: Experience with big data technologies and distributed computing.
• Cloud Services: Familiarity with cloud platforms such as AWS, Google Cloud, or Azure.
• DevOps: Knowledge of CI/CD pipelines and tools for automating the deployment of machine learning models.