Machine Learning Engineer - Tensorflow/PyTorch (5-8 yrs)

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.