Machine Learning Engineer (WFH)

Job Summary:

As a Machine Learning Engineer, you will work closely with data scientists, data engineers, and software developers to build scalable machine learning solutions. You will be responsible for designing, developing, and deploying machine learning models, as well as optimizing them for performance and scalability. The ideal candidate has a strong background in machine learning, statistical analysis, and software engineering, with experience in implementing real-world machine learning solutions.

Key Responsibilities:

• Model Development: Design, develop, and implement machine learning models and algorithms to solve business problems.Develops and implements machine learning models and algorithms to solve complex problems.
  

• Data Preprocessing: Clean, preprocess, and analyze large datasets to be used for training models.

• Feature Engineering: Identify and create meaningful features that improve model performance.

• Model Training & Evaluation: Train machine learning models, tune hyperparameters, and evaluate their performance using appropriate metrics.

• Model Deployment: Deploy machine learning models to production environments, ensuring scalability and reliability.

• Collaborate with Teams: Work closely with data scientists, software engineers, and other stakeholders to integrate machine learning models into existing systems.

• Research & Innovation: Stay updated with the latest advancements in machine learning, AI, and related fields, and apply new techniques to improve existing models and processes.

• Performance Monitoring: Monitor the performance of deployed models and update them as needed to ensure they continue to meet business needs.

• Documentation: Document models, processes, and results for future reference and to ensure transparency.

Qualifications:

• Education: Bachelor’s or Master’s degree in Computer Science, Data Science, Mathematics, Statistics, or a related field.

• Experience:

  • 2+ years of experience in machine learning, data science, or related roles.

  • Hands-on experience with machine learning frameworks such as TensorFlow, PyTorch, or Scikit-learn.

  • Proficiency in programming languages such as Python, R, or Java.

  • Experience with data preprocessing, feature engineering, and model evaluation.

• Skills:

  • Strong understanding of machine learning algorithms and techniques (e.g., regression, classification, clustering, deep learning).

  • Requires strong programming skills and expertise in data science.

  • Experience with data visualization tools such as Matplotlib, Seaborn, or Tableau.

  • Familiarity with cloud platforms such as AWS, Azure, or GCP.

  • Knowledge of version control systems such as Git.

  • Excellent problem-solving skills and the ability to work independently and in a team.

  • Strong communication skills to explain complex concepts to non-technical stakeholders.

Preferred Qualifications:

• Experience with big data technologies such as Hadoop, Spark, or Kafka.

• Knowledge of natural language processing (NLP) or computer vision.

• Experience with containerization and orchestration tools like Docker and Kubernetes.

• Familiarity with MLOps practices and tools.