Senior Machine Learning Engineer

Ensign InfoSecurity; Singapore; On-site; USD 80,000 - 130,000; Full time

Job summary

Join a forward-thinking company as a Senior Machine Learning Engineer, where your expertise in AI and machine learning will drive innovative solutions for Cyber Security. In this pivotal role, you'll design and optimize machine learning models, ensuring they are effectively developed and deployed in production environments. Collaborate with cross-functional teams to align models with business objectives, engage with customers to understand their needs, and stay ahead of advancements in technology. This is an exciting opportunity to make a significant impact in a dynamic and rapidly evolving field.

Qualifications

• Proven experience in developing and deploying machine learning models in production.
• Strong understanding of MLOps/DevOps principles and experience with CI/CD tools.

Responsibilities

• Design, develop, and optimize machine learning solutions for business problems.
• Collaborate with teams to integrate models into applications and services.

Skills

Machine Learning Model Development

Data Engineering

Python Programming

MLOps/DevOps Principles

Communication Skills

Education

Bachelor's Degree in Computer Science or related field

Master's Degree in Data Science or related field

Tools

TensorFlow

PyTorch

Scikit-Learn

Gitlab CI/CD

Docker

Kubernetes

AWS

Google Cloud

Azure

Job description

Ensign is committed to harnessing the power of artificial intelligence and machine learning to drive cutting-edge solutions for Cyber Security. We are looking for a skilled Senior Machine Learning Engineer with expertise in AI model building and model deployment to join our dynamic team. This role is crucial in ensuring that our machine learning models are effectively developed, optimized, and deployed to production environments.

Key Responsibilities:

Model Development:

• Design, develop, and optimize machine learning solutions tailored to solve specific business problems in the areas of Operational Technology (OT) Analytics and Threat Intelligence.
• Collaborate with data scientists and domain experts to understand data, define features, and build models using tools such as TensorFlow, PyTorch, or Scikit-Learn.
• Implement and test various machine learning algorithms, ensuring high accuracy, performance, and scalability.
• Ability to perform data engineering using tools such as Kafka, or OPC UA

Model Deployment:

• Develop and maintain CI/CD pipelines for deploying machine learning models to production environments.
• Work closely with DevOps and engineering teams to integrate models into applications and services.
• Monitor and manage deployed models to ensure optimal performance, scalability, and reliability.
• Automate the deployment process to minimize manual intervention and errors.
• Perform hyperparameter tuning, model evaluation, and validation to ensure robust and efficient models.
• Identify and resolve bottlenecks in model performance, optimizing for speed, accuracy, and resource utilization.

Business engagement and Communication:

• Engage with the customer to understand business requirements and craft innovative solutions
• Collaborate with cross-functional teams, including data engineering and software development to ensure alignment and integration of machine learning models with business objectives.
• Communicate model outcomes, limitations, and assumptions to stakeholders in a clear and concise manner.

Research and Innovation:

• Stay updated with the latest advancements in machine learning, AI, and related technologies.
• Experiment with new algorithms, tools, and frameworks to enhance the capabilities of our machine learning models.

Qualifications:

• Proven experience in developing and deploying machine learning models in production environments.
• Proficiency in programming languages such as Python, R, or Java.
• Experience with machine learning frameworks such as TensorFlow, PyTorch, Keras, or Scikit-Learn.
• Strong understanding of MLOps/DevOps principles and experience with tools like Gitlab CI/CD, MLFlow, Docker, Kubernetes, or similar.
• Familiarity with cloud platforms such as AWS, Google Cloud, or Azure, and experience in deploying models on these platforms.
• Excellent problem-solving skills and the ability to work independently and collaboratively.
• Strong communication skills and the ability to explain complex concepts to non-technical stakeholders.

Preferred Skills:

• Knowledge of big data technologies like Hadoop, Spark, or similar.
• Understanding of data preprocessing, feature engineering, and data visualization techniques.
• Experience with version control systems like Git.