Apply via This Form: https://forms.gle/wPDYmH6b4TKuEEbB9
🚨We're Hiring – Urgently Needed!🚨
As an AI Engineer, you will play a pivotal role in driving digital transformation and building enterprise-grade, secure AI solutions for the banking sector. You will design, deploy, and scale LLM-driven architectures, Retrieval-Augmented Generation (RAG) systems, and AI agents tailored for financial operations, risk evaluation, and elevated customer experiences.
Key Responsibilities
- LLM & Pipeline Architecture: Design, develop, and deploy end-to-end LLM pipelines, focusing on prompt engineering, RAG, and multi-agent orchestration for banking workflows.
- Knowledge & Vector Management: Build and maintain scalable vector databases and knowledge bases, leveraging optimal chunking, embedding, and reranking strategies to handle sensitive financial data.
- AI Governance & Reliability: Implement robust guardrails, input/output filtering, and hallucination mitigation strategies to maintain zero-trust reliability and regulatory compliance.
- Quality Evaluation & Iteration: Conduct continuous, measurable evaluations of AI model output quality, precision, and performance to drive data-led operational improvements.
- System Integration: Integrate AI models seamlessly into backend services via REST APIs and manage secure, container-based deployments.
Key Qualifications
- Education: Bachelor’s degree (S1) in Computer Science, Software Engineering, Data Science, or a related quantitative field.
- Experience: Minimum 2 years of fulltime experience developing and deploying Machine Learning or LLM-based systems in production environments (mandatory).
- Core Tech Stack: Advanced proficiency in Python and leading LLM frameworks (LangChain, LlamaIndex, or equivalent).
- Architecture & Storage: Strong experience with RAG architectures, embedding models, and vector stores (Milvus, Qdrant, pgvector, or equivalent).
- Deployment: Proven capability in API integration and containerized deployment (Docker, Kubernetes).
Banking & Enterprise Advantage (Nice to Have)
- Domain & Security: Deep understanding of data privacy, governance, and security protocols specific to banking regulations (e.g., OJK/BI compliance, PCI-DSS, GDPR).
- Advanced Machine Learning: Hands-on experience in fine-tuning open-source LLMs, MLOps practices, and observability platforms for AI services.
- Financial Analytics: Prior experience working with financial datasets, credit/risk evaluation systems, or fraud detection models.