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Remote, mid-level ML role with broad requirements creates strong candidate competition.
Requires specialized ML, deployment, and MLOps expertise, making background fit highly domain-specific.
Explicit 3–5 years plus mandatory ML, MLOps, and cloud skills enforces strict shortlisting filters.
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Design, develop, and deploy machine learning models (classification, regression, NLP, computer vision, time-series forecasting) to support business initiatives.
Implement model deployment and MLOps pipelines using Docker, CI/CD tools, and monitor production model performance.
Collaborate with cross-functional teams to translate business problems into AI/ML solutions and scale successful prototypes to production.
3–5 years of experience in machine learning model development and deployment.
Proficiency in Python and ML libraries such as TensorFlow or PyTorch.
Experience with model deployment tools and MLOps platforms (Docker, Kubernetes, MLflow, Airflow).
Familiarity with cloud ML services (AWS SageMaker, Azure ML, or GCP AI Platform).
Experienced in end-to-end AI/ML lifecycle including data preprocessing, feature engineering, model tuning, and production monitoring.
Able to work closely with software developers, data engineers, and business stakeholders to deliver scalable AI solutions.
Up-to-date with latest AI research and competent in experimenting with advanced models like LLMs and transformers.