ML Engineer
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ML Engineer
09 Dec 2025
Location: Jakarta
Position Type: Full-time
Reports to: CTO
About The Role
We are seeking an ML Engineer to design, build, and scale our machine learning and AI tools. With a particular focus on the ingestion and creation of documents. This role will work at the intersection of machine learning, large language models, and data engineering, enabling structured insight from complex financial and economic documents.
The ideal candidate will have strong software engineering fundamentals, practical experience building AI-driven data tools, and a passion for applying modern NLP/LLM techniques in production systems.
Key Responsibilities
- Work closely with our current web development team to integrate AI solutions with internal and client facing tools.
- Design and implement pipelines to extract structured data from financial reports, PDFs, and other unstructured text sources.
- Develop and fine-tune workflows combining SLMs (small language models), LLMs, and rule-based methods for accurate and explainable data extraction.
- Build scalable services and APIs for internal and client-facing consumption of extracted financial data.
- Integrate open-source tools and libraries (e.g., Hugging Face, LangChain, pdfplumber, Camelot, Tabula) and commercial LLM APIs where appropriate.
- Implement evaluation frameworks (accuracy, latency, cost) and maintain benchmarks for extraction quality.
- Ensure robust engineering practices: testing, version control, CI/CD pipelines, containerization, and cloud deployment.
- Contribute to research and stay up to date with advances in document AI, new libraries and new techniques.
- Education: BSc (or higher) in Computer Science, Software Engineering, or related field.
- Experience: 3+ years of professional experience as a Machine Learning Engineer, AI Engineer, or NLP Engineer.
- Demonstrated experience building tools that leverage LLMs/SLMs for text and document understanding.
- Strong programming skills in Python
- Familiarity with document parsing libraries
- Hands-on use of vector databases (e.g., Chroma, Pinecone, Weaviate) for retrieval-augmented generation.
- Strong background in software engineering best practices (Git, CI/CD, testing)
- Knowledge of financial reporting standards and typical financial statement structures.
- Experience deploying AI workloads in Microsoft Azure
- Familiarity with API design for ML deployment
- Ability to work collaboratively with business stakeholders.
- Excellent communication skills to explain AI model behaviour and limitations to non-technical teams.
- Opportunity to build a greenfield AI product in the mineral economics domain.
- Work with cutting-edge LLM and AI tools.
- Modern, central office near to public transport and key amenities
- Competitive salary
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