Machine Learning Engineer

Machine learning Engineer (Contract)

Location: Remote

Contract Duration: 6 months extendable

Interview Rounds: 2

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.