AI/ML Data Scientist

Job title: ML Engineer (with Data Science Background)

Location: Remote
mandatory Skills: Data Science, ML

Job Summary:

We are looking for a skilled Machine Learning Engineer with a strong background in Data Science to join our team. The ideal candidate started their career as a Data Scientist and has evolved into a hands-on ML Engineer, capable of taking models from experimentation to production. You'll be responsible for building scalable machine learning systems, designing deployment pipelines, and collaborating closely with data scientists, engineers, and product teams.

Key Responsibilities:

• Model Development & Deployment:
  • Convert data science prototypes into scalable and maintainable ML solutions.
  • Build and deploy machine learning models using frameworks like TensorFlow, PyTorch, or Scikit-learn.
• ML Infrastructure & Pipelines:
  • Design and develop ML pipelines (training, validation, inference) using tools like MLflow, Airflow, Kubeflow, or SageMaker.
  • Automate model retraining and deployment with CI/CD workflows.
• Productionization:
  • Containerize models using Docker and deploy to cloud-native environments (AWS, Google Cloud Platform, or Azure).
  • Optimize performance and latency for real-time or batch inference services.
• Data Engineering Support:
  • Collaborate with Data Engineers to build data pipelines for feature extraction, transformation, and monitoring.
  • Ensure high-quality, version-controlled datasets for training and inference.
• Monitoring & Maintenance:
  • Implement monitoring for model drift, accuracy, and performance.
  • Manage model lifecycle, including versioning, A/B testing, and rollback strategies.
• Cross-functional Collaboration:
  • Work with product managers, analysts, and engineers to translate business problems into ML solutions.
  • Communicate model behavior and performance to non-technical stakeholders.

Required Skills & Experience:

• Strong ML & DS Foundation:
  • 3+ years of experience in Data Science, with 1 2 years in ML Engineering.
  • Proficient in Python, and ML libraries (Scikit-learn, TensorFlow, PyTorch, XGBoost, etc.)
• ML Engineering & Ops:
  • Experience in deploying and monitoring ML models in production.
  • Knowledge of CI/CD, model serving (FastAPI, Flask, TorchServe), and container orchestration (Docker, Kubernetes).
• Data Pipeline & Cloud:
  • Familiar with data processing tools like Spark, Pandas, or Dask.
  • Experience with cloud platforms (AWS/Google Cloud Platform/Azure) and tools like S3, Lambda, SageMaker, BigQuery, etc.
• MLOps Tools:
  • Hands-on experience with ML lifecycle tools such as MLflow, Weights & Biases, or TFX.
• Version Control & DevOps: