Java Developer (AI Experience) - (Full-time)

Information Security Responsibilities

• Promote and enforce awareness of key information security practices, including acceptable use of information assets, malware protection, and password security protocols
• Identify, assess, and report security risks, focusing on how these risks impact the confidentiality, integrity, and availability of information assets
• Understand and evaluate how data is stored, processed, or transmitted, ensuring compliance with data privacy and protection standards (GDPR, CCPA, etc.)
• Ensure data protection measures are integrated throughout the information lifecycle to safeguard sensitive information

Key Responsibilities:

• Design and implement robust Java applications with integrated AI/ML capabilities.
• Develop and maintain scalable solutions using Java EE frameworks such as Spring and Hibernate.
• Collaborate with data scientists and engineers to integrate machine learning models into production systems.
• Write clean, efficient, and well-documented code following best practices.
• Utilize scripting languages (e.g., Python, JavaScript) for automation and data preprocessing tasks.
• Conduct performance tuning and optimization for AI-driven components.
• Stay updated on emerging AI/ML technologies and recommend innovative solutions.

Required Qualifications:

• 4–5 years of professional experience in Java development.
• Strong understanding of core Java concepts and object-oriented programming principles.
• Hands-on experience with Java EE frameworks (Spring, Hibernate).
• Moderate proficiency in scripting languages such as Python or JavaScript.
• Basic understanding of machine learning concepts and experience with relevant libraries (e.g., TensorFlow, Scikit-learn).
• Strong analytical and problem-solving skills.

Preferred Skills:

• Experience deploying AI/ML models in production environments.
• Familiarity with cloud platforms (AWS, Azure, GCP) and containerization (Docker, Kubernetes).
• Knowledge of RESTful APIs and microservices architecture.
• Exposure to big data technologies (Hadoop, Spark) is a plus.

Performance Metrics:

• Timely delivery of AI-integrated Java applications.
• Code quality and adherence to best practices.
• Successful integration and optimization of ML models.
• Collaboration and contribution to AI/ML innovation within the team.