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 Scikitlearn 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:
- ProblemSolving: Strong analytical skills with the ability to solve complex problems using datadriven approaches.
- Communication: Effective communication skills to articulate technical concepts to nontechnical stakeholders.
- Collaboration: Ability to work effectively in crossfunctional teams whether inperson or remotely.
- Adaptability: Willingness to learn and adapt to new technologies and methodologies in the AI/ML field.
(d) Experience Levels:
- Junior: 02 years of experience in AI/ML development or related fields.
- MidLevel: 35 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 cuttingedge 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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