Responsibilities
- Design, build, and deploy machine learning models in production environments
- Develop scalable data pipelines and feature engineering workflows
- Research, experiment, and implement advanced ML algorithms
- Optimize model performance, accuracy, and efficiency
- Collaborate with cross-functional teams to translate business requirements into ML solutions
- Monitor model performance and retrain models as needed
- Implement MLOps best practices including CI/CD for ML systems
- Ensure data quality, governance, and compliance standards
Basic qualifications
- 3+ years of experience in machine learning or AI development
- Strong programming skills in Python (preferred) or Java
- Experience with ML libraries such as TensorFlow, PyTorch, or Scikit-learn
- Strong understanding of supervised and unsupervised learning algorithms
- Experience with SQL and working with large datasets
- Familiarity with cloud platforms (AWS, Azure, or GCP)
- Knowledge of software engineering best practices (Git, testing, version control)
Preferred qualifications
- Experience with deep learning, NLP, or computer vision
- Familiarity with big data technologies (Spark, Hadoop)
- Experience deploying models using Docker and Kubernetes
- Knowledge of MLOps tools such as MLflow or Kubeflow
- Experience with REST APIs and microservices architecture
- Problem-solving and analytical thinking
- Strong mathematical and statistical foundation
- Communication and collaboration skills
- Ability to work in Agile environments
Tags & focus areas
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