Job Title: Machine Learning Engineer.
Experience Required: 7-10 Years.
Job Summary:
- We are seeking a Machine Learning Engineer with hands-on experience in designing, developing, and deploying ML models for real-world use cases.
- The ideal candidate will have strong coding skills, deep understanding of machine learning workflows, and the ability to integrate AI solutions into production environments.
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.
- Ability to work in agile, cross-functional teams.
