Practice Architect (Data-Related Technology)

Yêu cầu ứng viên Knowledge:• Architecture and Design Patterns: Expertise in software architecture (e.g., microservices, event-driven systems, and serverless architecture). Deep understanding of automation-friendly architecture frameworks and patterns (e.g., Infrastructure as Code, self-healing systems).• Data Engineering: Expertise in building scalable data pipelines and real-time streaming (e.g., Apache Kafka, Flink, Spark). Strong grasp of data orchestration tools (e.g., Apache Airflow, Prefect) and database optimization (SQL and NoSQL).• AI/ML Expertise: Knowledge of AI/ML frameworks (e.g., TensorFlow, PyTorch, Scikit-learn). Proficiency in the full AI/ML lifecycle, including data preparation, model training, deployment, and monitoring (e.g., MLOps pipelines). Understanding of AI-driven automation (e.g., intelligent process automation, predictive analytics, and autonomous systems).• Security and Compliance: Knowledge of secure AI, automation, and data systems (e.g., data governance, GDPR, SOC 2). Familiarity with automation for compliance monitoring and reporting.• Emerging Technologies: Awareness of cutting-edge trends in automation (e.g., hyper-automation, low-code/no-code platforms) and their integration with AI and data systems. Familiarity with technologies like edge computing, IoT automation, and generative AI (e.g., LLMs, GANs).Experience:• Technical Leadership: 10+ years in software engineering or technology roles, with at least 5 years in architecture leadership, covering data, AI, and automation.• AI/ML and Data Systems: Proven track record of delivering AI/ML-powered automation solutions, such as intelligent chatbots, fraud detection systems, or workflow optimization. Hands-on experience building and managing scalable data platforms and predictive analytics pipelines.• Cross-Functional Collaboration: Experience working closely with stakeholders across engineering, data science, operations, and business functions to deliver automation and AI solutions.• Mentorship: Guided engineering teams, data scientists, and automation specialists to adopt best practices in AI, data engineering, and automation.Competency:• Strategic Thinking: Ability to align Cloud, Automation, AI, and Data solutions with organizational goals and long-term strategies.• Systems Thinking: Expertise in designing solutions that integrate Cloud, AI, Automation, and Data ecosystems seamlessly.• Analytical Skills: Strong ability to solve complex automation and data challenges while ensuring efficiency and scalability.• Leadership and Influence: Proven track record of leading multi-disciplinary teams and influencing stakeholders to adopt automation and AI initiatives.• Agility and Adaptability: Comfortable navigating dynamic technological environments and adapting to emerging trends in Cloud, Automation, AI, and Data.• Execution Excellence: Track record of delivering reliable, scalable cloud-based/ automation systems and AI solutions on time and within budget.Personal Attributes:• Visionary: Forward-thinking and able to envision how automation, AI, and data can transform business processes and drive innovation.• Collaborative: Skilled at building relationships across teams to deliver integrated and impactful automation solutions.• Detail-Oriented: Focused on ensuring the quality, reliability, and security of automation, AI, and data systems.• Ethical: Committed to responsible use of AI and automation, ensuring compliance with ethical and regulatory standards.• Continuous Learner: Enthusiastic about exploring advancements in AI, data, and automation technologies and applying them to real-world problems.• Resilient: Able to handle challenges with persistence and adaptability, driving successful outcomes in high-pressure environments.