AI & Machine Learning Engineer

Job Purpose

To design, build, deploy, and maintain Artificial Intelligence (AI) and Machine Learning (ML) solutions that address real business needs, while operating the company’s AI platforms in a way that is effective, secure, and well governed. All work must align with internal policies, information security standards, data protection requirements (including UU PDP No. 27/2022), and applicable regulatory requirements.

Key Responsibilities

1. AI & Machine Learning Solution Development

• Translate business problems into AI/ML use cases and assess feasibility, data availability, and expected business benefit before development starts.

• Develop, train, and validate ML models for use cases such as classification, prediction/forecasting, anomaly detection, document processing, and natural language processing.

• Build Generative AI solutions, including LLM-based applications, prompt design, Retrieval-Augmented Generation (RAG), and API integration with internal systems.

• Evaluate models using appropriate metrics and baselines, test for bias and robustness, and document results and known limitations.

2. Data Preparation & Pipelines

• Collect, clean, and transform data from databases, enterprise applications (e.g., ERP), files, and APIs for training and inference.

• Build and maintain data pipelines and feature engineering workflows, with data quality checks.

• Ensure that data used for AI/ML is authorized and, where required, masked, anonymized, or otherwise protected before use.

3. Deployment, MLOps & Platform Operations

• Deploy models and AI services (e.g., as APIs) and integrate them with business applications in coordination with the application development and infrastructure teams.

• Apply version control to code, data, and models, and follow change management procedures before any release to production.

• Monitor model performance, drift, availability, latency, and usage cost; retrain, update, or retire models as needed.

• Administer AI platforms and applications: manage user access, roles, permissions, and configurations; perform first-level troubleshooting and coordinate with vendors or internal IT teams.

4. Business Support & User Acceptance Testing

• Gather and document business requirements together with user departments.

• Support testing and User Acceptance Testing (UAT) before AI solutions are deployed to production.

• Prepare user guides and assist users in using AI solutions effectively and in accordance with company policies.

• Identify new AI/ML use cases to improve productivity and business process efficiency.

5. AI Governance, Security & Compliance

• Maintain an inventory of AI applications, models, datasets, vendors, and use cases within the company.

• Help ensure that sensitive, confidential, customer, and personal data are not used in AI platforms or models without appropriate controls and approvals.

• Prepare model documentation (purpose, data sources, assumptions, limitations, owner) and maintain logs and audit trails to support explainability and human oversight.

• Support AI risk assessments and control documentation, including risks such as data leakage, prompt injection, and model misuse.

• Support compliance with IT policies, Information Security, Data Privacy, and AI Governance requirements, and support internal and external audits related to AI usage.

Minimum Qualifications

Education: Bachelor’s degree in Information Technology, Information Systems, Computer Science, Computer Engineering, Statistics, Mathematics, Data Science, or a related field.

Experience: 1 to 3 years of relevant experience in machine learning, data science, data engineering, or software development involving ML/AI. Fresh graduates with a strong portfolio (e.g., thesis, GitHub projects, competitions) may be considered for the junior level.

Required Technical Knowledge

• Python and SQL; data libraries such as pandas and NumPy; ML frameworks such as scikit-learn and PyTorch or TensorFlow.

• Solid fundamentals of machine learning and statistics (supervised and unsupervised learning, model evaluation, overfitting, feature engineering).

• Generative AI/LLMs, prompt engineering, RAG, vector databases, and REST APIs.

• Git and basic software engineering practices; basic cloud technologies and application integration.

• Fundamentals of information security and data privacy.

Advantages

• MLOps tools and practices (e.g., MLflow, Docker, workflow orchestration, CI/CD).

• Cloud AI services (e.g., Azure OpenAI/Azure ML, AWS SageMaker/Bedrock, Google Vertex AI) and enterprise AI platforms such as Microsoft Copilot, ChatGPT Enterprise, or Gemini.

• Familiarity with AI governance and security frameworks (e.g., NIST AI RMF, ISO/IEC 42001, ISO/IEC 27001).

• Experience in a regulated industry or with enterprise systems such as ERP; BI/visualization tools.