Key Responsibilities:

1. Model Development and Deployment:

Design, develop, and train machine learning models for specific use cases, such as classification, regression, clustering, natural language processing, or computer vision.
Optimize model performance and accuracy through hyperparameter tuning and advanced techniques.
Deploy machine learning models into production environments using tools such as TensorFlow Serving, PyTorch, Docker, or Kubernetes.

2. Data Preprocessing and Feature Engineering:

Extract, clean, and preprocess structured and unstructured data from various sources to make it suitable for ML models.
Perform feature engineering, selection, and transformation to enhance model performance.

3. Algorithm Research and Experimentation:

Explore and experiment with state-of-the-art machine learning and AI techniques, including deep learning, reinforcement learning, and transfer learning.
Stay up-to-date with the latest AI advancements and evaluate their applicability to business needs.

4. Software Development:

Develop and integrate ML pipelines and APIs for seamless interaction between models and applications.
Write clean, efficient, and maintainable code following best software engineering practices.

5. Collaboration and Communication:

Work closely with cross-functional teams, including data engineers, product managers, and domain experts, to define requirements and deliver solutions.
Clearly communicate technical findings and results to non-technical stakeholders.

6. Performance Monitoring and Maintenance:

Required Skills and Qualifications:

1. Technical Expertise:

Bachelor’s or Master’s degree in Computer Science, Data Science, Mathematics, or related field (Ph.D. preferred).
Strong understanding of machine learning principles, algorithms, and best practices.
Proficiency in programming languages such as Python, R, or Java.
Experience with ML frameworks and libraries like TensorFlow, PyTorch, Scikit-learn, Keras, or Hugging Face.

2. Data Management Skills:

Hands-on experience with data manipulation and analysis using tools like Pandas, NumPy, and SQL.
Knowledge of big data technologies such as Hadoop, Spark, or Kafka is a plus.

3. Deployment and Scaling:

Familiarity with cloud platforms like AWS, Azure, or Google Cloud for ML model deployment.
Understanding of containerization and orchestration tools (e.g., Docker, Kubernetes).

4. Analytical Skills:

5. Problem-Solving Ability:

Strong analytical mindset to solve complex problems and deliver scalable solutions.
Ability to work on multiple projects simultaneously while meeting deadlines.

6. Communication and Teamwork:

Preferred Qualifications:

Certifications in AI/ML technologies or cloud platforms (e.g., AWS Certified Machine Learning, Google Cloud ML Engineer).
Experience with natural language processing (NLP) or computer vision applications.
Knowledge of MLOps practices, CI/CD pipelines, and version control systems like Git.
Background in a specialized field such as robotics, autonomous systems, or financial modeling.

Work Environment:

A fast-paced, collaborative team environment with opportunities to work on cutting-edge AI technologies.
Access to advanced computational resources for model training and experimentation.
Opportunities for professional growth through training, conferences, and certifications.

Compensation and Benefits:

Job Type: Full-time

Pay: $150,000.00 - $190,000.00 per year

Benefits:

Schedule:

Work Location: Remote

Diagonal Matrix Ltd

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