Key Responsibilities:
AI and Machine Learning Development:
- Design, develop, and implement AI solutions using Python to solve complex business problems.
- Develop machine learning models using scikit-learn, TensorFlow, PyTorch, or other relevant frameworks.
- Work with large datasets and implement data preprocessing and feature engineering for AI model training and validation.
- Integrate AI models into production environments, ensuring scalability, performance, and reliability.
Python Development and Automation:
- Write efficient, reusable, and modular Python code for AI systems, data pipelines, and automation tasks.
- Implement APIs, data ingestion, and data transformation pipelines to integrate AI models into business processes.
- Optimize Python-based algorithms for high-performance and scalability, ensuring minimal latency in production environments.
Model Training and Evaluation:
- Train, test, and validate machine learning models using appropriate algorithms and frameworks.
- Perform model evaluation and optimization, including hyperparameter tuning, model validation, and performance testing.
- Continuously improve model accuracy and efficiency by iterating on different algorithms and architectures.
Collaboration and Stakeholder Engagement:
- Collaborate with cross-functional teams, including data scientists, business analysts, and product managers, to understand business requirements and deliver AI-driven solutions.
- Communicate complex technical concepts to non-technical stakeholders, ensuring alignment with business goals and objectives.
- Participate in agile development cycles, providing updates, feedback, and troubleshooting support as needed.
Data Analysis and Insights:
- Analyze business and operational data to extract insights and identify trends, providing actionable recommendations to improve decision-making processes.
- Develop visualization tools and reports to present the results of AI models and analytics to stakeholders.
- Leverage AI to automate data-driven tasks and decision-making processes across the organization.
Model Deployment and Maintenance:
- Deploy machine learning models into production environments using containerization (e.g., Docker, Kubernetes) and cloud technologies (e.g., AWS, Azure, Google Cloud).
- Monitor and maintain deployed models, addressing performance issues and updating models as needed to ensure continuous optimization.
- Ensure data security, privacy, and compliance when deploying AI solutions, particularly for sensitive business data.
Continuous Learning and Improvement:
- Stay updated with the latest trends, technologies, and research in the fields of AI, machine learning, and Python development.
- Attend training, conferences, or webinars to expand knowledge and skills in AI technologies and Python programming.
- Contribute to knowledge sharing within the team by providing technical insights, documentation, and best practices.
Required Qualifications:
- Bachelor's degree in Computer Science, Data Science, Software Engineering, or a related field.
- 2-4 years of hands-on experience with Python development in an AI/ML context.
- Solid understanding of machine learning algorithms and frameworks such as scikit-learn, TensorFlow, Keras, PyTorch, etc.
- Experience in data preprocessing, feature engineering, and working with structured and unstructured data.
- Familiarity with cloud platforms (e.g., AWS, Google Cloud, Microsoft Azure) and their AI/ML offerings.
- Strong knowledge of data structures and algorithms, with experience writing efficient and scalable Python code.
- Experience in model deployment, monitoring, and continuous improvement of AI models.
- Ability to work with APIs and integrate AI models into production systems.
Skills Required
Tensorflow, Keras, Pytorch, Aws