Machine Learning Engineer

Overview

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