Machine Learning Engineer

Job Responsibilities:

Data Collection and Preparation:

• Collaborate with cross-functional teams, including domain experts, to collect and preprocess relevant data from multiple sources (e.g., healthcare records, financial transactions).
• Conduct thorough data analysis, cleaning, and feature engineering to transform raw data into usable formats for model development.
• Ensure data privacy and security compliance when handling sensitive data.

Machine Learning Model Development:

• Design, build, and implement machine learning models tailored to solve specific business challenges
• Select appropriate datasets and data representation methods for various projects (e.g., time-series data, structured/unstructured data).
• Experiment with and optimize algorithms (e.g., supervised learning, unsupervised learning, anomaly detection) to improve model accuracy and efficiency.
• Perform statistical analysis, test different hypotheses, and fine-tune models using test results.

Model Optimization and Evaluation:

• Evaluate model performance using various metrics (accuracy, precision, recall, F1 score, etc.) and apply techniques such as cross-validation, hyperparameter tuning
• Continuously improve models based on feedback, new data, and evolving business requirements.
• Ensure models can scale effectively in production environments.

Model Deployment and Monitoring:

• Deploy machine learning models into production using cloud services (AWS, GCP, Azure) or containerized environments (Docker, Kubernetes).
• Oversee and monitor the performance of models in real-time, ensuring they provide accurate and actionable insights.
• Implement model retraining and continuous learning processes to maintain performance over time.

Research and Innovation:

• Stay current with the latest developments in machine learning, AI, and relevant technologies by actively researching and experimenting with new algorithms, models, and techniques.
• Propose and integrate novel approaches into existing workflows to improve model accuracy, efficiency, and scalability.

Required Qualifications:

Technical Skills:

• Programming Languages

  : Proficiency in Python, Java, or R, with strong expertise in Python for machine learning tasks.

• Machine Learning Frameworks

  : Familiarity with TensorFlow, PyTorch, Keras, and scikit-learn for developing and optimizing models.

• Mathematics & Statistics

  : Strong foundation in linear algebra, calculus, probability, and statistics to understand and apply machine learning algorithms effectively.

• Big Data Technologies

  : Experience working with tools like Hadoop, Apache Spark for handling large datasets.

• Cloud Computing

  : Hands-on experience with cloud platforms such as AWS, GCP, or Azure for deploying and maintaining machine learning models.

• Version Control & Deployment

  : Proficient with version control tools like Git and experience in CI/CD pipelines for model deployment.

Domain-Specific Knowledge (Desired):

• Healthcare/Medical Data

  : Understanding of medical datasets (e.g., patient records, diagnostic imaging) and how to build models for healthcare applications (e.g., predicting neurological disorders).

• Financial Data/Fraud Detection

  : Experience with financial transaction data and building systems for detecting fraudulent activity and financial anomalies (e.g., Anti-Money Laundering models).

Soft Skills:

• Problem-Solving

  : Strong analytical and problem-solving skills, with the ability to approach challenges in creative and innovative ways.

• Communication

  : Excellent written and verbal communication skills, with the ability to explain complex machine learning concepts to non-technical stakeholders.

• Team Collaboration

  : Ability to work collaboratively in an interdisciplinary team, sharing knowledge and working towards common goals.