Ai Ml Engineer

Key Responsibilities:

1. AI/ML Model Development:
• Design, build, and train machine learning models to address specific business problems and use cases.
• Work with Python and machine learning libraries such as scikit-learn, TensorFlow, Keras, and PyTorch to develop models.
• Implement data preprocessing and feature engineering techniques to prepare datasets for machine learning.
2. Cloud Deployment and Management:
• Deploy machine learning models to cloud platforms AWS (e.g., SageMaker, EC2, S3) and GCP (e.g., AI Platform, Vertex AI) for scalable and efficient production use.
• Assist in setting up cloud-based infrastructure to support AI and ML solutions.
• Ensure models are optimized for performance, reliability, and cost-effectiveness when running in cloud environments.
3. Collaboration and Communication:
• Work closely with data scientists, software engineers, and product teams to define project goals, requirements, and timelines.
• Communicate AI/ML model results, techniques, and insights clearly to both technical and non-technical stakeholders.
• Participate in team discussions, code reviews, and knowledge-sharing sessions to promote continuous learning.
4. Model Monitoring and Optimization:
• Monitor the performance of deployed models and provide recommendations for improvement based on results.
• Continuously optimize models to ensure they meet business requirements and function well in real-world scenarios.
5. Data Handling and Analysis:
• Handle large datasets, perform data cleaning, transformation, and normalization as part of the model-building process.
• Assist in the exploration and analysis of data to discover trends, patterns, and correlations useful for developing machine learning solutions.
6. Continuous Learning and Development:
• Stay up-to-date with the latest trends, technologies, and research in AI and machine learning.
• Apply new techniques and algorithms to improve model accuracy, efficiency, and scalability.
• Engage in learning and development opportunities to enhance your skills and knowledge in AI/ML.

Skills & Qualifications:

• Education: Bachelors degree in computer science, Data Science, Engineering, Mathematics, or a related field.
• Experience:
• 1-3 years of hands-on experience in machine learning, artificial intelligence, or data science roles.
• Proficiency in Python and familiarity with machine learning libraries such as scikit-learn, TensorFlow, PyTorch, Keras, etc.
• Familiarity with AWS (e.g., SageMaker, EC2, S3) and GCP (e.g., AI Platform, Vertex AI) for deploying machine learning models.
• Knowledge of basic machine learning concepts, such as supervised and unsupervised learning, classification, regression, and clustering.
• Technical Skills:
• Proficient in Python and machine learning libraries (e.g., TensorFlow, Keras, scikit-learn, Pandas, NumPy).
• Experience with cloud platforms like AWS and GCP.
• Familiarity with version control tools like Git for code management.
• Exposure to containerization technologies (e.g., Docker) and cloud computing.
• Soft Skills:
• Strong communication skills to articulate technical concepts and model results clearly.
• Ability to work effectively in a team and collaborate with colleagues from various disciplines.
• Eagerness to learn, improve, and adapt to new technologies and industry trends.