• Seniority: Senior (9+ years of relevant experience).
• Educational Background: Bachelor’s or Master’s degree in Computer Science, Data Science, or a related field.
• Advanced Python Proficiency: Strong expertise in Python for data processing, scripting, and automation.
• Cloud Computing Experience: Hands-on experience with cloud platforms such as Azure, AWS, or GCP.
• Data Management: Proficiency in RDBMS/NoSQL data stores and an understanding of their appropriate use cases.
• Data as Code: Experience with version control, regular commits, unit tests, CI/CD, and containerization tools such as Docker and Kubernetes.
• AI Architecture Expertise: Proven experience in designing and implementing AI architectures within cloud environments.
• AI Ethics Knowledge: In-depth understanding of AI ethics, bias mitigation strategies, and responsible AI practices.
• Problem-Solving Skills: Exceptional problem-solving abilities and attention to detail.
• Communication Skills: Excellent communication skills with the ability to engage stakeholders, gather requirements, and translate them into actionable technical plans. Fluency in English is required.

Optional

• Infrastructure as Code (IaC): Familiarity with IaC practices and tools is a plus.

• Data Engineering and Pipeline Development:
• • Design and Build Pipelines: Create and maintain robust data pipelines that are foundational to supporting AI/ML models.
• • Data Quality Assurance: Ensure data quality and integrity at every stage of the data lifecycle.
• • Workflow Optimization: Enhance data workflows for optimal performance and scalability.
• AI Architecture in Cloud Environments:
• • Cloud AI Design: Architect and implement AI solutions in cloud environments, including AWS, Azure, or GCP.
• • Integration and Deployment: Work closely with cloud engineers to ensure smooth integration and deployment of AI models.
• • Utilize Cloud-Native Tools: Leverage cloud-native services and tools to enhance AI capabilities and streamline operations.
• Deployment of Large Language Models (LLMs):
• • Model Deployment: Deploy and fine-tune large language models (LLMs) to meet various business needs.
• • Scalability and Security: Ensure that LLM deployments are scalable, efficient, and secure within production environments.
• • Performance Monitoring: Continuously monitor and optimize model performance in real-time applications.
• AI Ethics and Responsible AI Practices:
• • Ethical AI Development: Integrate ethical considerations and bias mitigation strategies into AI processes.
• • Promote Responsible AI: Advocate for and ensure the adoption of responsible AI practices across all projects.
• • Stay Informed: Keep abreast of the latest AI ethics guidelines and industry best practices.
• Stakeholder Communication and Requirement Gathering:
• • Collaborate with Stakeholders: Engage with stakeholders to understand business objectives and translate them into technical specifications.
• • Effective Communication: Convey complex technical concepts to non-technical stakeholders in an understandable manner.
• • Project Updates: Provide regular progress updates and insights on AI projects to stakeholders.