Job Description

Role Overview:

As a Machine Learning Engineer, you will be responsible for designing, developing, and deploying machine learning models that drive [specific application, e.g., predictive analytics, natural language processing, computer vision, recommendation systems, etc.]. You will collaborate closely with data scientists, software engineers, and product teams to build scalable, high-performance AI solutions.

You should have a strong foundation in machine learning algorithms, data processing, and software development, along with the ability to take ownership of the full machine learning lifecycle-- from data collection and model training to deployment and monitoring.

Key Responsibilities:

• Model Development: Design and implement machine learning models for various applications, such as [insert specific use cases, e.g., predictive analytics, classification, clustering, anomaly detection, etc.].
• Data Preparation & Processing: Work with large datasets, including preprocessing, feature engineering, and data augmentation to ensure high-quality input for model training.
• Model Training & Tuning: Train, optimize, and fine-tune models using modern machine learning frameworks and algorithms. Monitor performance metrics and adjust parameters for model improvement.
• Model Deployment: Deploy machine learning models into production environments using tools like Docker, Kubernetes, or cloud platforms such as AWS, Azure, or Google Cloud.
• Performance Monitoring & Optimization: Continuously monitor deployed models for performance, accuracy, and scalability. Implement model retraining pipelines and maintain model health.
• Collaboration: Work cross-functionally with data scientists, software engineers, product managers, and other stakeholders to integrate machine learning solutions into business workflows.
• Research & Innovation: Stay up-to-date with the latest trends and advancements in machine learning and AI to drive innovation within the company.

Qualifications:

• Education: Bachelor's or Master's degree in Computer Science, Data Science, Statistics, Applied Mathematics, or a related field. A PhD is a plus but not required.
• Experience:
• 0-1years of experience in machine learning, data science, or a related technical role.
• Proven experience building and deploying machine learning models in a production environment.
• Experience with cloud platforms (e.g., AWS, GCP, Azure) for model deployment and scalability.
• Technical Skills:
• Proficiency in Python (preferred) or other programming languages (e.g., R, Java, C++).
• Strong knowledge of machine learning frameworks and libraries such as TensorFlow, PyTorch, Scikit-learn, Keras, or similar.
• Familiarity with deep learning techniques (e.g., CNNs, RNNs, transformers) is highly desirable.
• Experience with data processing tools such as Pandas, NumPy, and SQL.
• Knowledge of version control (e.g., Git), containerization (e.g., Docker), and CI/CD pipelines.
• Experience with big data tools and frameworks (e.g., Hadoop, Spark) is a plus.
• Soft Skills:
• Strong problem-solving and analytical skills.
• Excellent communication and collaboration skills.
• Ability to work independently and manage multiple priorities in a fast-paced environment.
• Detail-oriented and organized, with a passion for learning and innovation.

Job Type: Full-time

Pay: ?5,000.00 - ?30,000.00 per month

Schedule:

• Day shift

Work Location: In person