Machine Learning Engineer

Machine Learning Engineer

We are seeking a Senior Machine Learning Engineer with 5–7 years of experience in designing, developing, deploying, and maintaining machine learning models and AI-driven solutions. The ideal candidate should have strong expertise in Python, machine learning algorithms, and ML frameworks, with experience building scalable ML pipelines and deploying models into production environments.

Responsibilities

The Machine Learning Engineer will be responsible for designing, developing, training, and optimizing machine learning models to solve complex business problems. This role involves working with both structured and unstructured datasets to identify patterns and generate actionable insights. The engineer will implement supervised, unsupervised, and deep learning algorithms and deploy these models into production environments. Collaboration with data scientists, data engineers, architects, and business stakeholders is crucial to ensure that the models meet business needs and are integrated effectively into enterprise applications.

• Design, develop, train, and optimize machine learning models to solve business problems.
• Perform data preprocessing, feature engineering, model selection, and evaluation.
• Develop scalable ML pipelines and integrate models into enterprise applications.
• Work with structured and unstructured datasets to identify patterns and generate insights.
• Implement supervised, unsupervised, and deep learning algorithms.
• Deploy and monitor ML models in production environments.
• Collaborate with data scientists, data engineers, architects, and business stakeholders.
• Optimize model performance, accuracy, scalability, and reliability.
• Implement model versioning, monitoring, and continuous improvement processes.
• Follow best practices for code quality, testing, documentation, and model governance.

Requirements

• Python & Machine Learning: Strong Python programming skills with hands-on experience in ML algorithms, Scikit-learn, TensorFlow, or PyTorch.
• Data Science & Model Development: Experience in data preprocessing, feature engineering, statistical analysis, model training, evaluation, and optimization.
• ML Deployment & MLOps: Experience deploying ML models into production, developing ML pipelines, model monitoring, and working with cloud platforms (AWS/Azure/Google Cloud Platform).

• Cloud Platforms: Experience with AWS, Azure, or Google Cloud Platform for deploying and managing ML models.
• Big Data Technologies: Familiarity with big data technologies such as Hadoop, Spark, or Kafka.
• Containerization: Experience with Docker and Kubernetes for deploying ML models in containerized environments.
• Communication Skills: Strong ability to communicate complex technical concepts to non-technical stakeholders.
• Problem-Solving: Excellent problem-solving skills and the ability to work independently as well as in a team.