AI Engineer

At Globe, our goal is to create a wonderful world for our people, business, and nation. By uniting people of passion who believe they can make a difference, we are confident that we can achieve this goal.

Build a robust enterprise engineering foundation, designing and deploying scalable software systems that integrate next-generation AI innovations and reusable services. Collaborate in multi-disciplinary teams to design, maintain, and support modern developer platforms, ensuring system architecture is highly extensible and future-ready. Champion software engineering best practices while actively exploring ways to enhance systems with AI capabilities.

Solid Engineering Foundation: Develop, scale, and maintain core software platforms, emphasizing clean architecture, high availability, and API security. Applied AI Focus: Build and deploy robust integrations with Small and Large Language Models, embedding intelligent components into user workflows. Broad System Integration: Design reusable components and microservices that connect seamlessly with legacy environments and cloud frameworks. Modern DevOps Handover: Partner with operations and infrastructure teams to enable seamless, automated software delivery, monitoring, and scaling. Technical Optimizations: Analyze, benchmark, and optimize platform latency, data delivery pipelines, and overall code execution efficiency. Innovation Enablement: Maintain a flexible architecture open to emerging tech, ensuring rapid experimentation and deployment of modern AI capabilities. Deployment, Integration & Testing: Integration of AI Models, reusable products/modules to existing applications and systems with focus on scalability, reliability, and security. Monitoring & Maintenance: Continuously monitor performance of deployed AI models and refine as we see fit. Collaboration: Work closely with cross-functional teams to translate business requirements into actionable AI solutions. Code Quality: Write clean, maintainable, and efficient code following best practices and coding standards. Documentation: Create and maintain comprehensive documentation to describe AI solutions and system designs. Continuous Enablement: Stay updated with the latest advancements in Artificial Intelligence, Machine Learning, and LLMs. Support: Provide technical support and troubleshoot issues as needed to ensure smooth operation of AI solutions. Key Performance Indicators (KPIs) Model Accuracy & Performance: Percentage of correct predictions; avoiding hallucinations. Evaluation of classification models. Reusability: Ability to create AI products that will be reused across different projects, groups, and divisions. Data Quality & Management: Ensuring maximum data efficiency, consistency, and structural utility. Customer & Stakeholder Feedback: Satisfaction levels of internal/external stakeholders and measured business value. Business Impact: ROI for AI solutions, user adoption rates, and operational efficiency improvements. Top Deliverables Deployed AI Systems & Products: Creation of AI reusable modules and SDKs that accelerate AI adoption across Globe. AI Models: Trained and optimized models for specific tasks like language processing and predictive analytics. Data Pipelines & Infrastructure: Robust data pipelines feeding high-quality data; understanding cloud infrastructure requirements. Technical Docs & Enablement Reports: Clear documentation on model architecture, training, configurations, and stakeholder enablement. Skills & Certifications Soft Skills Communication: Excellent written & verbal skills to convey tech solutions to all audiences. Mentorship: Mentoring engineers through proactive knowledge sharing and collaborative support. Hard Skills Software Engineering: Strong foundation in clean code, API development, and modern system architectures. AI & Data: Practical exposure to LLMs, LangChain, RAG frameworks, and data manipulation. Languages: Python, Node.js. Plus: