Senior Machine Learning Engineer

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Job Overview

Senior Machine Learning Engineer

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.