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. Job Description 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.DUTIES AND RESPONSIBILITIES: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 DeliverablesDeployed 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 & CertificationsSoft SkillsCommunication: Excellent written & verbal skills to convey tech solutions to all audiences.Mentorship: Mentoring engineers through proactive knowledge sharing and collaborative support.Hard SkillsSoftware 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: React, FastAPI, Typescript, Go.Cloud & DevOps: AWS, GCP, GitOps, CI/CD, Terraform.Certifications (Nice-to-Have)Data Science & ML EngineeringGenerative AI Fundamentals & for DevelopersData Analytics / Data EngineeringFoundational Cloud (e.g., Cloud Digital Leader)CompetenciesCore CompetenciesProblem-Solving: Strong analytical skills to resolve complex software issues, interpret patterns, and make data-driven decisions.Technical Expertise: Solid software engineering fundamentals paired with understanding of ML/DL, Python, DevOps, Cloud, and Kubernetes.Attention to Detail: Focused on code quality, robust system functionality, and performance.Support CompetenciesCollaboration: Working effectively in cross-functional teams.Adaptability: Rapid learning of emerging AI tools and commitment to continuous skill-building.Time Management: Efficiently multi-tasking across complex projects.REQUIREMENTS:EducationBachelor’s degree in Computer Science, Software Engineering, AI, ML, Data Science, or a related technical field.Experience2+ years of core software development experience with a strong systems or backend engineering foundation.Baseline familiarity or exposure to AI/ML, LLMs, and RAG, staying highly open to modern AI innovations.Track record of delivering scalable, production-grade solutions.PortfolioDemonstrable portfolio of previous software and AI/ML projects that show clear business value to stakeholders.Equal Opportunity EmployerGlobe’s hiring process promotes equal opportunity to applicants, Any form of discrimination is not tolerated throughout the entire employee lifecycle, including the hiring process such as in posting vacancies, selecting, and interviewing applicants.Globe’s Diversity, Equity and Inclusion Policy Commitment can be accessed hereMake Your Passion Part of Your Profession. Attracting the best and brightest Talents is pivotal to our success. If you are ready to share our purpose of Creating a Globe of Good, explore opportunities with us.