As a Machine Learning Engineer, you will play a crucial role in the end-to-end development and deployment of machine learning models. Your main focus will be on data preprocessing, feature engineering, model training, and deployment using cloud-based solutions, particularly Google Cloud Platform (GCP) and Vertex AI. You will work closely with a team of senior engineers, data scientists, and business stakeholders to ensure that the models you build are optimized, scalable, and production-ready.
This role is ideal for a candidate with a strong foundation in Python programming, cloud services, and API development. You should also have experience deploying models in a cloud environment and a good understanding of ML Ops best practices.
Key Responsibilities
- Collaborate with senior engineers and data scientists to develop machine learning solutions for real-world applications.
- Perform data preprocessing and feature engineering to improve model performance and efficiency.
- Train, test, and optimize machine learning models using Python-based ML frameworks such as TensorFlow, PyTorch, or Scikit-Learn.
- Deploy and manage models on Google Cloud Platform (GCP) using Vertex AI.
- Develop and integrate APIs to serve models in production environments, ensuring seamless real-time predictions.
- Implement ML Ops best practices, including model monitoring, version control, and CI/CD pipelines to streamline deployment.
- Work with large-scale datasets to extract insights and improve decision-making processes.
- Troubleshoot and fine-tune models to enhance accuracy and overall performance.
- Stay updated with the latest trends and advancements in machine learning, cloud computing, and AI technologies.
Required Skills & Experience
- 3+ years of experience working as a Machine Learning Engineer or in a similar role.
- Strong proficiency in Python and hands-on experience with ML libraries such as TensorFlow, PyTorch, Scikit-Learn, Pandas, and NumPy.
- Experience working with Google Cloud Platform (GCP), specifically Vertex AI for model training, deployment, and monitoring.
- Knowledge of API development and experience deploying ML models into production environments.
- Familiarity with ML Ops best practices, including CI/CD pipelines, model versioning, and performance monitoring.
- Experience working with large-scale datasets and optimizing model performance for scalability.
- Strong problem-solving skills and the ability to work independently in a remote setting.
- Excellent communication skills, with the ability to collaborate effectively with cross-functional teams.
Why Consider This Opportunity?
- 100% Remote. Work from anywhere with a flexible schedule.
- Contract with High Potential for Extension. Initial 6-month contract, with a strong likelihood of extension based on performance and project needs.
- Collaborative Environment. Work alongside a team of experienced data scientists and engineers on cutting-edge machine learning projects.
- Exposure to Advanced AI/ML Technologies. Hands-on experience with GCP, Vertex AI, and cloud-based ML deployment.
- Professional Growth. Gain deep experience in ML Ops, cloud computing, and real-world AI model implementation.
If you are passionate about machine learning and excited about working on real-world AI solutions, this could be the perfect opportunity for you!