Machine Learning Engineer

3 days ago

Mohali, Punjab, India Datatobiz Full time ₹ 12,00,000 - ₹ 36,00,000 per year

Machine learning Engineer (Contract)

Location: Remote

Contract Duration: 6 months extendable

Interview Rounds: 2

Experience: 5+ yrs

Relevant Experience: 5+ yrs

IST work timings  10AM - 7PM

Immediate joiners

Key Responsibilities:

  • Identify and define machine learning use cases across business domains (e.g., prediction, classification, recommendation, NLP, computer vision).
  • Design and implement end-to-end ML workflows, from data ingestion and feature engineering to model training, evaluation, and deployment.
  • Develop reusable and scalable ML pipelines using tools such as MLflow, Airflow, Kubeflow, or Vertex AI.
  • Write efficient and maintainable Python code leveraging frameworks such as TensorFlow, PyTorch, Scikit-learn, and FastAPI.
  • Perform data analysis, preprocessing, and feature extraction using Pandas, NumPy, and SQL.
  • Implement model monitoring, versioning, and retraining workflows to ensure continuous model improvement.
  • Collaborate with data engineers, product managers, and software developers to integrate ML solutions into production systems.
  • Document experiments, code, and workflows to ensure reproducibility and scalability.

Technical Skills Required:

  • Programming: Python (mandatory), familiarity with Java or R is a plus.
  • Machine Learning: Regression, Classification, Clustering, NLP, Deep Learning, LLM fine-tuning.
  • Frameworks & Libraries: TensorFlow, PyTorch, Scikit-learn, Hugging Face Transformers.
  • Data Tools: Pandas, NumPy, SQL, Spark (optional).
  • MLOps Tools: MLflow, Airflow, Docker, Kubernetes, Git, CI/CD pipelines.
  • Cloud Platforms: AWS Sagemaker, GCP Vertex AI, or Azure ML.
  • Version Control: GitHub/GitLab.

Workflow & Project Experience:

  • Built and deployed end-to-end ML pipelines for predictive analytics, recommendation engines, and NLP applications.
  • Experience in model lifecycle management experimentation, validation, deployment, and monitoring.
  • Exposure to data versioning, model drift detection, and continuous improvement processes.
  • Strong understanding of workflow automation using Airflow/Kubeflow pipelines.
  • Hands-on experience integrating ML models with APIs using FastAPI/Flask for real-time inference.

Soft Skills:

  • Strong analytical thinking and problem-solving ability.
  • Excellent communication and documentation skills.

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