Job Description

Senior Machine Learning Engineer - 3 Year Contract

Key Responsibilities:

• Model Development & Optimization: Design, develop, and optimize machine learning models for real-world applications, ensuring high accuracy, scalability, and efficiency.
• ML Pipeline & Deployment: Build and maintain scalable ML pipelines using cloud platforms (AWS, Azure, GCP) and containerization technologies (Docker, Kubernetes).

• Feature Engineering & Data Processing: Collaborate with data engineers to preprocess, clean, and transform large datasets for training and inference.

• Productionization: Deploy ML models into production, monitor performance, and continuously improve them through A/B testing and retraining.

• Collaboration: Work closely with cross-functional teams including software engineers, product managers, and business stakeholders to align ML solutions with business objectives.

• MLOps & Automation: Implement MLOps best practices, automate model training and deployment, and ensure reproducibility.

• Performance Monitoring: Develop and maintain monitoring tools to track model performance, drift, and reliability in production.

• Research & Innovation: Stay updated with the latest trends and advancements in AI/ML, and integrate cutting-edge research into business solutions.

Required Qualifications & Skills:

• Education: Bachelors or Masters degree in Computer Science, Data Science, Machine Learning, or a related field. A Ph.D. is a plus.

• Experience: Minimum 5+ years of experience in machine learning, deep learning, and AI model deployment in production environments.

• Programming: Strong proficiency in Python, with experience in libraries like TensorFlow, PyTorch, Scikit-learn, Pandas, and NumPy.

• Cloud & Infrastructure: Hands-on experience with cloud services (AWS, GCP, Azure) and MLOps tools like Kubeflow, MLflow, or SageMaker.

• Big Data & Databases: Experience with Spark, Hadoop, SQL, and NoSQL databases for handling large-scale datasets.

• DevOps & CI/CD: Familiarity with Git, Docker, Kubernetes, and CI/CD pipelines for ML model deployment.

• Algorithm Development: Strong knowledge of ML algorithms, deep learning architectures (CNNs, RNNs, Transformers), and optimization techniques.

• Problem-Solving: Strong analytical and problem-solving skills with the ability to design innovative ML solutions for complex business challenges.

• Excellent Communication: Ability to explain technical concepts to non-technical stakeholders and document ML processes effectively.

Preferred Qualifications:

• Experience with NLP, Computer Vision, or Reinforcement Learning.

• Hands-on experience with AutoML, hyperparameter tuning, and model interpretability.

• Experience with real-time ML applications and edge AI.

• Contributions to open-source ML frameworks or research publications.