Gather and analyze user requirements related to Artificial Intelligence (AI) solutions and design appropriate technical approaches to meet business needs.
Design, develop, test, and monitor Artificial Intelligence (AI) and Machine Learning (ML) models for various use cases, including but not limited to prediction, classification, segmentation, and process automation.
Develop end-to-end AI/ML pipelines, covering data collection, model training, evaluation, deployment, and production monitoring.
Collaborate in the development of web applications or systems that integrate AI components using Python and other AI frameworks or tools.
Follow and comply with the organization's Software Development Life Cycle (SDLC) processes and standards.
Develop responsive and user-friendly user interfaces.
Design, build, and manage databases.
Design, develop, and implement APIs for system integration.
Perform application troubleshooting and debugging, and document identified issues along with their corresponding resolutions.
Collaborate with software developers, data analysts, and business teams to ensure AI solutions align with business requirements and corporate policies.
Work closely with internal and external stakeholders to design and implement scalable software architecture and AI solutions.
Actively participate as a technical support member in solution development projects.
Prepare and maintain comprehensive project documentation and other technical documentation.
Research and recommend emerging AI and Machine Learning technologies that can be adopted to improve business operations and innovation.
Provide recommendations for policies, guidelines, and procedures related to the ethical, secure, and responsible use of Artificial Intelligence technologies.
Design and execute AI model evaluation processes using appropriate performance metrics, and continuously improve models based on evaluation results.
Monitor the performance of deployed AI models to ensure reliability, accuracy, and operational stability.
Implement security best practices for AI solutions, including access control, sensitive data protection, input validation, and mitigation of AI model misuse or abuse.
Optimize AI solution performance in terms of model accuracy, inference latency, computational resource utilization, and operational cost efficiency.
Integrate AI solutions with internal and external systems through RESTful APIs, message brokers, databases, or other integration mechanisms.
Design and execute comprehensive testing for AI solutions, including Functional Testing, Model Validation, Prompt Testing, Regression Testing, and User Acceptance Testing (UAT).
Develop, manage, and optimize knowledge bases, vector databases, embeddings, and Retrieval-Augmented Generation (RAG) mechanisms to support AI-powered applications.
Requirement:
Bachelor's Degree (S1) or equivalent in Computer Science, Information Technology, Computer Engineering, or a related field.
Experience in Artificial Intelligence (AI) projects or AI-related activities is preferred. Fresh graduates with relevant AI project, internship, or thesis experience are welcome to apply.
Strong understanding of the Software Development Life Cycle (SDLC), including both Waterfall and Agile methodologies.
Experience participating in AI/ML projects using Agile and/or Waterfall development methodologies.
Ability to collaborate effectively with cross-functional teams (Data, Business, and IT) and possess excellent communication skills.
Proficient in Microsoft Office Suite (Word, Excel, and PowerPoint).
Strong programming, algorithmic thinking, and problem-solving skills.
Experience designing databases, writing SQL queries, stored procedures, and database functions using Microsoft SQL Server 2019 and MySQL.
Strong proficiency in Python, particularly for AI development using libraries such as Pandas, NumPy, and Scikit-learn.
Familiarity with web development frameworks such as Django and Flask for integrating AI models into web applications.
Familiarity with front-end technologies such as HTML5, Bootstrap, jQuery, CSS, React.js, and AngularJS.
Familiarity with server operating systems, including Windows Server and Linux.
Hands-on experience with AI/ML frameworks such as TensorFlow, PyTorch, Scikit-learn, and Generative AI frameworks such as LangChain, LlamaIndex, or similar technologies.
Strong understanding of Large Language Models (LLMs), Prompt Engineering, Retrieval-Augmented Generation (RAG), embeddings, and vector databases.
Understanding of MLOps concepts, including model deployment, versioning, monitoring, and AI model lifecycle management.
Familiarity with cloud platforms for AI/ML development, particularly Amazon Web Services (AWS). Experience with Microsoft Azure and/or Google Cloud Platform (GCP) is considered an advantage.
Proficient in using Git or other version control systems, as well as CI/CD pipelines for collaborative software development.
Familiarity with containerization technologies such as Docker for AI application development and deployment.
Strong understanding of AI application security, data protection, and Responsible AI principles.
Understanding of AI/ML model evaluation metrics and the ability to analyze model performance to support continuous improvement.
Strong understanding of RESTful APIs, with the ability to design, develop, and manage APIs for AI model deployment and integration.
Strong troubleshooting and debugging skills.
Ability to prepare and maintain comprehensive technical documentation, including AI model documentation, API documentation, and other project documentation.
Detail-oriented with excellent attention to accuracy and quality.
Strong quality-focused mindset with a commitment to delivering reliable and scalable AI solutions.
Ability to manage multiple projects simultaneously while effectively prioritizing tasks and meeting deadlines.
Ability to write clean, modular, maintainable, and well-documented code that adheres to software development best practices and can be easily maintained by other team members.
