Machine Learning Engineer

7 days ago

India HacktivSpace Community Full time

About HacktivSpace:

HacktivSpace is a tech organization, specializing in leveraging machine learning, artificial intelligence, and data analytics to solve complex problems across various domains.

Role Overview:

We're seeking a Machine Learning Engineer to design, implement, and deploy machine learning models and algorithms. This role demands expertise in Python, TensorFlow, PyTorch, and other related technologies. The engineer will collaborate with cross-functional teams to deliver scalable solutions.

Key Responsibilities:

1. Algorithm Development: Design and implement machine learning algorithms, including supervised and unsupervised learning, deep learning, and reinforcement learning. Tech Stack: Python, TensorFlow, PyTorch, Scikit-learn

2. Data Preprocessing and Feature Engineering: Clean, preprocess, and analyze large datasets. Implement feature engineering techniques to improve model performance. Tech Stack: Pandas, NumPy, SciPy

3. Model Training and Evaluation: Train, validate, and fine-tune machine learning models. Utilize performance metrics such as accuracy, precision, recall, and F1-score. Tech Stack: TensorFlow, PyTorch

4. Deployment and Integration: Deploy models into production environments. Integrate with existing systems using Docker, Kubernetes, and Flask for scalability and reliability.

Tech Stack: Docker, Kubernetes, Flask

5. Continuous Learning and Optimization: Stay updated with advancements in machine learning. Continuously optimize models based on feedback and performance metrics.

Tech Stack: TensorFlow Extended (TFX), MLflow

6. Collaboration and Communication: Collaborate with cross-functional teams to understand requirements and deliver solutions. Communicate findings effectively.

Qualifications:

Bachelor's or Master's degree in Computer Science, Engineering, Mathematics, or related field.

Proficiency in Python, TensorFlow, PyTorch, and related libraries/frameworks.

Strong understanding of machine learning concepts and algorithms, with hands-on experience in data preprocessing, feature engineering, model training, and evaluation.

Experience with cloud platforms (e.g., AWS, Azure, GCP) and distributed computing frameworks (e.g., Apache Spark) is advantageous.

Strong problem-solving skills and ability to thrive in a fast-paced, collaborative environment.

Effective communication skills.

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