Job description

Qualifications & Skills:

(a) Educational Background:

Bachelor’s or Master’s degree in Computer Science, Engineering, Mathematics, or a related field.
Relevant certifications or courses in AI/ML, Data Science, or related fields are a plus.

(b) Technical Skills:

Programming Languages: Proficiency in Python, R, or similar languages used in AI/ML development.
Machine Learning Frameworks: Experience with frameworks such as TensorFlow, PyTorch, Scikit-learn, or Keras.
Data Handling: Strong skills in handling and processing large datasets, with proficiency in tools like Pandas, NumPy, and SQL.
Model Development: Experience in developing, training, and deploying machine learning models, including supervised and unsupervised learning techniques.
Algorithms: Understanding of fundamental AI/ML algorithms, including neural networks, decision trees, clustering, and regression.
Deep Learning: Familiarity with CNNs, RNNs, LSTMs, GANs, and other deep learning architectures.
Cloud Services: Experience with cloud platforms like AWS, Google Cloud, or Azure for AI/ML model deployment.
Version Control: Proficient in using version control tools such as Git.

(c) Additional Skills:

Problem-Solving: Strong analytical skills with the ability to solve complex problems using data-driven approaches.
Communication: Effective communication skills to articulate technical concepts to non-technical stakeholders.
Collaboration: Ability to work effectively in cross-functional teams, whether in-person or remotely.
Adaptability: Willingness to learn and adapt to new technologies and methodologies in the AI/ML field.

(d) Experience Levels:

Junior: 0-2 years of experience in AI/ML development or related fields.
Mid-Level: 3-5 years of experience with a proven track record in AI/ML projects.
Senior: 6+ years of experience, with leadership experience in AI/ML project management and model deployment.

Responsibilities:

(a) Model Development:

Design, develop, and implement machine learning models and algorithms tailored to specific business needs.
Continuously improve model performance by experimenting with different techniques and parameters.

(b) Data Analysis & Processing:

Collect, clean, and preprocess large datasets from various sources to feed AI/ML models.
Conduct exploratory data analysis to uncover trends, patterns, and insights.

(c) Deployment & Integration:

Deploy and integrate AI/ML models into existing systems or new applications, ensuring scalability and reliability.
Monitor model performance in production environments and implement improvements as needed.

(d) Collaboration & Communication:

Collaborate with data engineers, software developers, and business analysts to translate business requirements into AI/ML solutions.
Communicate findings and recommendations to stakeholders through reports, presentations, and visualizations.

(e) Research & Innovation:

Stay updated with the latest advancements in AI/ML technologies and methodologies.
Experiment with cutting-edge AI/ML tools and techniques to explore new opportunities for innovation.

(f) Documentation:

Maintain comprehensive documentation of models, processes, and results to ensure reproducibility and knowledge sharing.
Contribute to the development of best practices and standards for AI/ML development within the team.

(g) Mentorship (For Senior Roles):

Provide guidance and mentorship to junior AI/ML developers, helping them to grow their technical skills and advance in their careers.
Lead AI/ML projects and manage teams to ensure successful delivery of solutions.

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