Machine Learning (ML)Role Overview
We are seeking an experienced Machine Learning Engineer to design, develop, deploy, and scale machine learning solutions in production environments. The ideal candidate will have strong expertise in model development, MLOps, backend integration, and scalable ML system architecture.
Key Responsibilities
- Design, train, and deploy machine learning and deep learning models for real-world applications.
- Build and scale ML models and pipelines to handle large datasets and high-traffic production workloads.
- Perform data exploration, preprocessing, feature engineering, and dataset optimization.
- Develop and integrate APIs for ML model consumption using frameworks such as FastAPI, Flask, or Django.
- Implement end-to-end ML workflows, including experimentation, versioning, deployment, and monitoring.
- Build scalable data pipelines and processing workflows for batch and real-time use cases.
- Monitor model performance, drift, and system reliability in production.
- Collaborate with data engineers, DevOps, and product teams to productionize ML solutions.
Required Skills & Qualifications
- 5+ years of experience in Machine Learning Engineering or related roles.
- Proven experience in training, developing, deploying, and scaling ML models in production.
- Strong knowledge of data exploration, preprocessing, and feature engineering techniques.
- Proficiency in Python and ML/Data Science libraries such as Pandas, NumPy, Scikit-learn, TensorFlow, and PyTorch.
- Experience with API development and backend integration (FastAPI, Flask, Django).
- Solid understanding of MLOps tools and practices, including MLflow, Kubeflow, Airflow, Prefect, Docker, and Kubernetes.
- Experience building scalable data pipelines, processing workflows, and monitoring deployed models.
Shubham Vishwakarma
Full Stack Developer - Averlon
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