Back
Popular ML role with broad tool and deployment requirements but modest employer brand, creating medium competition.
Core ML engineering skills transfer broadly across industries, so background fit sensitivity is low.
Explicit 1–2 year requirement plus mandatory Python and ML frameworks yields medium strictness.
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Own the end-to-end ML lifecycle from prototyping to deployment and monitoring of models, including maintenance of observability dashboards.
Collaborate with PMs, data scientists, and cross-functional teams to translate business goals into measurable ML objectives and align on responsible ML practices.
Pilot new ML tools/frameworks and lead their integration into production environments.
Bachelor’s degree in Computer Science, Data Science, IT, or related field; Master’s preferred for senior levels.
1–2 years of work experience in data science or ML roles with hands-on experience using frameworks like scikit-learn or PyTorch.
Proficiency in Python is mandatory; familiarity with SQL, TensorFlow, Docker, Kubernetes, MLflow, Jupyter, and deployment tools like Flask/FastAPI is also required.
Ability to work under Agile or DevOps workflows and experience with data pipelines and ML deployment pipelines.
Capable of handling full ML model lifecycle including data cleaning, feature engineering, model training, deployment, and monitoring with minimal supervision.
Comfortable collaborating across distributed, cross-functional teams to integrate ML solutions and communicate technical concepts to non-technical stakeholders.
Experienced in developing scalable ML models and working with modern ML tools, CI/CD, and cloud services in a production environment.