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.

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 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: React, FastAPI, Typescript, Go.

• Cloud & DevOps: AWS, GCP, GitOps, CI/CD, Terraform.

Certifications (Nice-to-Have)

• Data Science & ML Engineering

• Generative AI Fundamentals & for Developers

• Data Analytics / Data Engineering

• Foundational Cloud (e.g., Cloud Digital Leader)

Competenc