ML Engineer

Requirements

Machine Learning (ML) Engineer

Key Responsibilities

• Model Development & Deployment
• Design, develop, and implement machine learning models tailored to business requirements.
• Work on end-to-end ML lifecycle including data preprocessing, feature engineering, model selection, training, validation, and deployment.
• Optimize models for performance, accuracy, and scalability in production environments.
• Data Engineering & Processing
• Collaborate with data engineers to ensure robust data pipelines for structured and unstructured data.
• Perform data wrangling, cleaning, and feature extraction to prepare high-quality datasets.
• Implement ETL workflows to ensure data is readily available for ML tasks.
• Collaboration & Stakeholder Management
• Partner with product managers, business analysts, and domain experts to translate business problems into ML solutions.
• Collaborate with cross-functional teams to integrate ML models into existing applications, platforms, or workflows.
• Provide technical guidance to junior engineers and contribute to knowledge-sharing within the team.
• Research & Innovation
• Stay updated on the latest research, tools, and frameworks in machine learning, deep learning, and AI.
• Experiment with cutting-edge techniques such as NLP, computer vision, or reinforcement learning when relevant to business needs.
• Continuously improve ML infrastructure, monitoring, and automation to increase efficiency.
• Testing, Monitoring & Maintenance
• Implement rigorous testing methodologies including A/B testing, model validation, and bias detection.
• Monitor model performance post-deployment to ensure stability and reliability over time.
• Troubleshoot and retrain models as required to maintain accuracy in dynamic environments.
  

Required Skills & Qualifications

• Bachelor's or Master's degree in Computer Science, Data Science, AI, or a related field.
• Minimum 3+ years of professional experience in machine learning engineering or applied ML.
• Strong proficiency in Python and popular ML/DL frameworks such as TensorFlow, PyTorch, or Scikit-learn.
• Solid understanding of machine learning algorithms, statistical modeling, and predictive analytics.
• Hands-on experience with data preprocessing, feature engineering, and pipeline building.
• Familiarity with SQL/NoSQL databases and data handling in large-scale systems.
• Experience with cloud platforms (AWS, GCP, Azure) and ML services for deployment.
• Strong problem-solving skills, with the ability to work independently and in collaborative team environments.
  

Preferred Skills (Good To Have)

• Exposure to Natural Language Processing (NLP), Computer Vision, or Reinforcement Learning.
• Experience in MLOps practices including CI/CD pipelines, model versioning, and monitoring tools.
• Familiarity with big data technologies like Spark, Hadoop, or Kafka.
• Knowledge of Docker, Kubernetes, or other containerization/orchestration tools.