Overview
Artificial Intelligence Engineer Jobs in Jakarta, Indonesia at Aetosky
Title: Artificial Intelligence Engineer
Company: Aetosky
Location: Jakarta, Indonesia
NLP/AI Engineer
The NLP/AI Engineer owns the intelligence logic layer of Aetosky's platform – the models and algorithms that determine what matters in a high-volume stream of multilingual open-source data. This is a dedicated AI/ML role: you design the statistical filters, build the semantic analysis pipeline, architect LLM-powered deep processing workflows, and lay the groundwork for transitioning to sovereign, air-gapped language models. A separate engineering role handles data ingestion infrastructure, allowing you to focus entirely on model performance, prompt engineering, evaluation, and cost-efficient AI at scale. AI-assisted development (GitHub Copilot, Cursor, Claude Code, or equivalent) is the standard workflow – not optional – and will be directly assessed during the hiring process.
Responsibilities
Core NLP / AI Responsibilities
• Design, implement, and refine text scoring and anomaly detection algorithms for identifying emerging trends and threats across multilingual data sources.
• Build and optimize semantic similarity pipelines: embedding model selection, vector-based content deduplication, and clustering for efficient human review.
• Develop detection logic for coordinated inauthentic behavior, including timing-based anomalies and content duplication patterns.
• Architect multi-step LLM inference workflows for deep analysis: intent extraction, entity identification, relationship mapping, and structured output generation.
• Iterate rapidly on prompt design and context management using AI-assisted tooling.
Model Performance & Cost Optimization Responsibilities
• Design evaluation frameworks and metrics for NLP output quality: precision, recall, false positive rates, and processing latency.
• Implement budget-aware processing controls that gracefully degrade under cost pressure without losing critical signals.
• Optimize LLM inference costs through prompt engineering, batching, caching, and token management strategies.
• Benchmark and evaluate models (commercial APIs and open-source alternatives) for cost-performance tradeoffs across target languages.
Sovereign AI & Research Responsibilities
• Establish the technical roadmap for transitioning from commercial LLM APIs to sovereign, air-gapped Small Language Models (SLMs) for sensitive deployments.
• Design data collection and annotation strategies to turn accumulated regional language data into fine-tuning datasets.
• Evaluate and prototype candidate SLM architectures for Southeast Asian and Middle Eastern languages and dialects.
• Monitor for adversarial data quality issues such as semantic drift and corpus contamination.
Collaboration Responsibilities
• Lead the platform's post-launch calibration process, translating analyst feedback on output quality into measurable system improvements.
• Collaborate with infrastructure and frontend engineering on data schemas, API contracts, and integration points.
• Document model decisions, prompt templates, and tuning parameters to support team scaling and knowledge transfer.
Classifications / Qualifications
Required
• 3+ years in NLP, machine learning engineering, or applied AI with a focus on production systems.
• Demonstrated daily proficiency with AI-assisted development tools (GitHub Copilot, Cursor, Claude Code, or equivalent) — this will be assessed in the technical evaluation.
• Deep hands-on experience with text embedding models, vector similarity search, and clustering algorithms.
• Strong LLM prompt engineering: multi-step prompt design, context window management, structured output control, and inference cost optimization.
• Strong Python skills with production experience in NLP/ML libraries (spaCy, Hugging Face Transformers, scikit-learn, or equivalent).
• Experience designing evaluation frameworks and quality metrics for NLP systems.
• Comfortable working autonomously across research and production in a small, high-ownership team.
Preferred
• Experience with multilingual NLP.
• Experience fine-tuning or training Small Language Models for domain-specific applications.
• Background in influence operation detection, disinformation analysis, or social media intelligence.
• Experience with semantic drift detection or adversarial data quality monitoring.
• Familiarity with government cloud environments and data residency requirements (FedRAMP, ISO 27001, or equivalent).
• Published research or demonstrated contributions in applied NLP, information extraction, or computational social science.
