About the Role
We are seeking a skilled Machine Learning Engineer to design, build, and deploy production-grade ML models that drive innovation across our products and services. You will work closely with data scientists, software engineers, and business stakeholders to translate cutting-edge research into scalable, real-world applications. This role is ideal for someone who thrives at the intersection of data science, software engineering, and problem-solving.
Key Responsibilities
- Design, develop, and optimize machine learning models for [industry use case — e.g., predictive analytics, natural language processing, computer vision].
- Collaborate with data scientists to prototype algorithms and transform them into scalable production systems.
- Build and maintain data pipelines and model training workflows.
- Apply best practices in MLOps, including versioning, monitoring, and continuous deployment of models.
- Work with software engineers to integrate ML models into customer-facing applications.
- Conduct performance testing, validation, and error analysis to ensure robustness and fairness.
- Stay up to date with the latest ML frameworks, research, and tools (e.g., PyTorch, TensorFlow, Hugging Face, Scikit-learn).
Qualifications
Required:
- Bachelor’s or Master’s in Computer Science, Data Science, Engineering, or related field.
- Strong programming skills in Python (plus familiarity with Java, C++, or R a bonus).
- Hands-on experience with ML frameworks (e.g., TensorFlow, PyTorch, Scikit-learn).
- Solid understanding of data structures, algorithms, and distributed systems.
- Experience with data pre-processing, feature engineering, and model evaluation.
- Familiarity with cloud platforms (AWS, GCP, or Azure) and containerization (Docker/Kubernetes).
Preferred:
- Experience with MLOps tools (MLflow, Kubeflow, Airflow, SageMaker).
- Background in NLP, computer vision, or reinforcement learning.
- Prior experience deploying models at scale in a production environment.
- Strong collaboration and communication skills.
