Machine Learning Engineer (The Model Innovator)

Are you passionate about solving complex problems with cutting-edge machine learning techniques? Do you love transforming raw data into intelligent systems that can make predictions, automate processes, and provide deep insights? If you're excited about building scalable, high-performance machine learning models that drive business innovation, then our client has the perfect opportunity for you. We’re looking for a Machine Learning Engineer (aka The Model Innovator) to develop, deploy, and optimize machine learning models that transform how we leverage data for decision-making.

As a Machine Learning Engineer at our client, you will work alongside data scientists, software engineers, and product managers to build machine learning solutions that power intelligent applications and services. You’ll be responsible for creating scalable algorithms, refining model performance, and ensuring that our AI systems deliver high-quality results in real-world environments.

Key Responsibilities:

1. Design and Develop Machine Learning Models:
• Build and deploy machine learning models using algorithms such as regression, classification, clustering, and deep learning. You’ll work with large datasets to train models that solve real-world problems like prediction, recommendation, and automation.
2. Model Training and Hyperparameter Tuning:
• Experiment with different model architectures and optimize hyperparameters to improve model accuracy and efficiency. You’ll apply cross-validation, regularization, and other techniques to ensure high-performing models.
3. Data Processing and Feature Engineering:
• Collaborate with data engineers and scientists to preprocess, clean, and transform large datasets into formats that are suitable for machine learning. You’ll perform feature engineering to extract meaningful features that enhance model performance.
4. Deploy Models into Production:
• Implement machine learning models in production environments, ensuring that they are scalable, reliable, and efficient. You’ll work with cloud platforms and DevOps teams to deploy models using technologies like Docker, Kubernetes, and CI/CD pipelines.
5. Monitor and Improve Model Performance:
• Continuously monitor model performance in production, detecting issues such as model drift or degradation. You’ll retrain and optimize models as needed, ensuring that they remain accurate and relevant over time.
6. Collaborate with Cross-Functional Teams:
• Work closely with software developers, product managers, and data scientists to integrate machine learning models into products and services. You’ll ensure that AI solutions meet business objectives and deliver measurable value.
7. Stay Current with AI and Machine Learning Trends:
• Keep up-to-date with the latest developments in machine learning, deep learning, and AI. You’ll explore new algorithms, tools, and techniques to continuously improve the machine learning solutions you develop.

Requirements

Required Skills:

• Machine Learning Expertise: Strong knowledge of machine learning algorithms, including supervised and unsupervised learning techniques. You’re experienced with tools like TensorFlow, PyTorch, Scikit-learn, and Keras for building and deploying models.
• Programming and Software Development: Proficiency in programming languages such as Python, R, or Scala, with experience writing production-level code. You can build, test, and deploy machine learning solutions efficiently.
• Data Engineering and Feature Engineering: Hands-on experience with data preprocessing, feature selection, and engineering. You understand how to handle large datasets and optimize them for machine learning workflows.
• Model Deployment and DevOps: Experience deploying machine learning models into production using cloud platforms (AWS, GCP, Azure) and containerization tools like Docker. You know how to implement models that scale efficiently.
• Collaboration and Communication: Excellent collaboration skills, with the ability to work closely with cross-functional teams to translate business requirements into machine learning solutions. You can explain technical concepts clearly to non-technical stakeholders.

Educational Requirements:

• Bachelor’s or Master’s degree in Computer Science, Data Science, AI, or a related field. Equivalent experience in machine learning engineering is highly valued.
• Certifications or additional coursework in machine learning, AI, or data science are a plus.

Experience Requirements:

• 3+ years of experience in machine learning engineering, with hands-on experience building and deploying machine learning models in production environments.
• Proven track record of working with large datasets, designing machine learning pipelines, and delivering AI-driven solutions that solve business problems.
• Experience working with cloud-based AI services (AWS SageMaker, Google AI Platform, Azure ML) is highly desirable.

Benefits

• Health and Wellness: Comprehensive medical, dental, and vision insurance plans with low co-pays and premiums.
• Paid Time Off: Competitive vacation, sick leave, and 20 paid holidays per year.
• Work-Life Balance: Flexible work schedules and telecommuting options.
• Professional Development: Opportunities for training, certification reimbursement, and career advancement programs.
• Wellness Programs: Access to wellness programs, including gym memberships, health screenings, and mental health resources.
• Life and Disability Insurance: Life insurance and short-term/long-term disability coverage.
• Employee Assistance Program (EAP): Confidential counseling and support services for personal and professional challenges.
• Tuition Reimbursement: Financial assistance for continuing education and professional development.
• Community Engagement: Opportunities to participate in community service and volunteer activities.
• Recognition Programs: Employee recognition programs to celebrate achievements and milestones.