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Remote-friendly, popular ML title, mid-level experience, metro location, and broad skills increase competition.
Core ML engineering and MLOps skills are broadly transferable across industries, with moderate domain specificity.
Explicit 2–6 years plus mandatory ML, cloud, Docker, and deployment skills make filters strict.
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Own the entire ML lifecycle: design, train, deploy, and monitor production models used by real customers across fintech, agri-tech, and SME sectors.
Build and optimize supervised, unsupervised, and deep learning models with scalable data/ML pipelines for ETL, training, and inference.
Collaborate with product and engineering teams to integrate ML models into APIs and applications using MLOps best practices including CI/CD and model monitoring.
2–6 years of hands-on experience in machine learning, data science, or ML engineering with production deployments.
Bachelor's degree in Computer Science, AI, Electrical Engineering, or equivalent experience.
Strong Python programming skills with libraries like NumPy, Pandas, scikit-learn, and deep learning frameworks (PyTorch or TensorFlow).
Experience with cloud platforms (AWS/GCP/Azure), containerization (Docker), Git-based CI/CD pipelines, and strong SQL skills with understanding of data modeling.
Experience operating end-to-end ML systems with strong ownership over production deployments and monitoring.
Technical proficiency in both data engineering and ML model development, including MLOps practices and collaborative integration within product teams.
Comfortable working in a fast-moving, hybrid/remote environment with cloud-native tooling and modern development workflows.