Head of AI Model Research and Development

Role Purpose

The Head of AI Model Research and Development will be responsible for end-to-end development of Large Language Models (LLMs) tailored for Indonesia and Indonesian languages & dialects further customized to handle specialized industry use cases, including banking, mining, telecom, manufacturing, and public sector applications. This role requires deep expertise in data strategy, dataset procurement, data cleaning, model training, fine-tuning, evaluation, and production deployment. The ideal candidate will architect scalable AI/ML pipelines, and ensure model governance, compliance, and accuracy for enterprise environments. This is a senior strategic role involving collaboration with business leaders, data engineers, product teams, and external data providers. This position will drive innovation, establish best practices for model training pipelines, evaluate emerging foundation models, and ensure the delivery of safe, explainable, and high-performance LLM models aligned with enterprise expectations. The ultimate goal is to deliver an accurate & culturally relevant state-of-the-art Indonesian LLM aligned with the organization’s vision of “AI for ALL.”

Key Responsibilities

1. LLM Strategy & Architecture Define the technical roadmap for building industry-specific LLMs in Bahasa Indonesia. Select training, fine-tuning, and RAG pipelines appropriate separately for each use case (e.g., banking risk assessment, mining safety documentation, customer service automation). Evaluate foundation models (open-source and proprietary) and recommend the best fit per domain. Architect scalable model-training infrastructure (GPU clusters, vector DBs, data lakes).

2. Data Procurement, Curation & Governance Identify and procure domain-specific datasets: e.g. banking documents, SOPs, mining safety manuals, regulatory guidelines, chat logs, PDF archives, email patterns, and transactional text data. Design data-collection frameworks that comply with data privacy, regulatory requirements, and industry certifications. Build pipelines for cleaning, labeling, anonymizing, deduplicating, clustering, and tokenizing large-scale corpora. Establish metadata standards, dataset versioning, data lineage, and governance processes.

3. Model Training, Fine-Tuning & Evaluation Lead the end-to-end training of custom LLM models using modern techniques (SFT, RLHF, DPO, distilled models, adapters/LoRA). Design evaluation datasets specific to each industry: compliance tests, financial domain QA, mining hazard queries, conversational benchmarks, multilingual fluency assessments, etc. Optimize performance for latency, accuracy, alignment, safety, and cost efficiency. Build monitoring pipelines for model drift, hallucination analysis, and post-deployment performance.

4. Responsible AI, Ethics & Security Implement guardrails, red-teaming, safety layers, and data anonymization. Ensure adherence to international AI governance frameworks. Drive transparent, explainable AI methodologies for regulated industries.

5. Leadership & Collaboration Lead and mentor data scientists, ML engineers, annotators, and model evaluators. Present AI strategies to senior stakeholders and convert business needs into technical solutions. Work with vendors, cloud partners, and universities for continuous talent and dataset pipeline development.

Qualification & Experience Education Required Master’s degree or PhD in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Computational Linguistics, or related field. Additional certifications in ML/AI (e.g., Deep Learning Specialization, MLOps, Cloud ML Architect) are preferred. Strong grounding in statistics, optimization, NLP, and distributed systems.

Experience Required: Minimum 15+ years of experience in Data Science, NLP, Machine learning and AI Solution Engineering. Proven track record of leading the end-to-end development and successful deployment of large-scale machine learning models, specifically LLMs. Prior work in regulated industries such as banking, mining, telco, finance, or public sector is highly preferred. Hands-on experience with Python, PyTorch, TensorFlow, Hugging Face, LangChain, vector DBs, GPU environments, and MLOps tooling. Extensive hands-on experience with the entire model lifecycle, including pre-training, fine-tuning, RLHF, and evaluation on massive datasets. Strong background in data engineering, including curating, cleaning, and processing large-scale unstructured text data for model training. Experience in defining and implementing monetization strategies for AI services, such as API-based models or “Token as a service.”

Preferred: Specific, demonstrable experience in building or adapting language models for non-English languages, ideally Indonesian or related languages. A history of successful collaboration with platform engineering and product teams to integrate complex AI models into production environments. A strong portfolio of relevant publications in top-tier AI conferences (e.g., NeurIPS, ICML, ACL) or significant contributions to major open-source AI projects.