Infosysta

Job Title: Senior Machine Learning Engineer Location: Remote Experience: 5+ Years Job Overview We are seeking a highly skilled Senior Machine Learning Engineer with strong expertise in time-series, sensor, and behavioral data to join our growing engineering team. You will be responsible for designing, developing, and deploying advanced ML models—particularly those integrated with wearable data, fitness tracking platforms, and real-time analytics pipelines. The ideal candidate combines deep technical ML knowledge with hands-on backend engineering experience to deliver scalable, production-ready solutions. Key Responsibilities Design, train, and deploy machine learning and deep learning models with a focus on time-series, sensor, and behavioral datasets. Build robust data ingestion, transformation, and processing pipelines for wearable and fitness tracking data. Implement real-time analytics solutions and deploy ML models in production environments. Develop and integrate backend services and APIs using Node.js to support ML-driven features across web and mobile platforms. Apply fraud detection and anomaly detection techniques to identify spoofed, manipulated, or inconsistent activity data. Collaborate with cross-functional teams (Product, Data Engineering, Backend, Mobile) to integrate ML capabilities into end-user experiences. Ensure scalable and secure ML infrastructure across cloud platforms (AWS, GCP, or Azure). Optimize models for edge inference where needed, considering privacy, latency, and performance constraints. Conduct statistical analysis, feature engineering, and rigorous model evaluation to drive actionable insights. Adopt and enforce modern MLOps best practices for CI/CD, experiment tracking, model versioning, monitoring, and automated deployment. Required Technical Skills Strong foundation in machine learning and deep learning frameworks such as PyTorch or TensorFlow . Expertise working with time-series , sensor , and behavioral data . Experience integrating data from wearables , IoT devices, and fitness tracking platforms. Proficient in Python for model development and in Node.js for backend development and integration. Strong understanding of fraud/anomaly detection methodologies. Hands-on experience building scalable data pipelines , streaming systems, and real-time analytics solutions. Familiarity with MLOps tools and practices (e.g., MLflow, Kubeflow, Docker, Kubernetes, CI/CD pipelines). Knowledge of cloud ML infrastructure across AWS, GCP, or Azure, including deployment, monitoring, and scaling. Understanding of data privacy , edge inference , and secure ML model operations. Qualifications Bachelor’s or Master’s degree in Computer Science, AI/ML, Data Science, or a related field. 5+ years of hands-on experience in ML engineering, model deployment, and backend integration. Strong communication skills and ability to collaborate across multidisciplinary teams. A track record of shipping ML models to production at scale is highly preferred.