Machine Learning Engineer
4 weeks ago
Key Responsibilities :
1. Lead Machine Learning Projects :
Take full ownership of machine learning projects from start to finish, including problem definition, data collection, model development, and deployment.
2. Algorithm Development : Develop and optimize machine learning algorithms using techniques such as deep learning, natural language processing, computer vision, and reinforcement learning.
3. Data Preparation : Pre-process and transform large datasets to create high-quality training data for model development.
4. Model Evaluation : Implement testing and validation procedures to ensure the performance, accuracy, and scalability of machine learning models.
5. Deployment and Integration : Collaborate with cross-functional teams to deploy machine learning models into production environments and integrate them with existing systems.
6. Collaboration with DevOps Team : Work closely with the DevOps team, having a basic understanding of version control systems (Git), CI/CD pipelines, and tools like Terraform.
Required Skills :
- Python DS | DE | ETL: Minimum of 2 years of hands-on experience.
- Advanced Python: Proven hands-on experience in advanced Python coding and libraries.
- Machine Learning: Experience in developing machine learning models and deploying them in production.
- ML Frameworks: Familiarity with tools like Scikit-learn, TensorFlow, or PyTorch.
- Azure Cloud: Strong experience working on Azure cloud services and tools.
- Databricks: Experience with Databricks for big data processing and machine learning pipelines.
- DevOps Knowledge: Basic understanding of DevOps practices, CI/CD pipelines, and version control.
- Deep Learning: Understanding of deep learning principles and techniques.
- NLP & Computer Vision: Basic understanding of Natural Language Processing (NLP) and Computer Vision.
- Python for Image Processing: Experience or understanding of image processing libraries like OpenCV.
Preferred Skills :
- Agile Methodology: Familiarity with Agile frameworks and practices.
- Experience working in a collaborative, team-based environment with cross-functional teams.
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