Foundational AI Engineer — Finance, LLMs & MLOps

Kapital Consulting; Sydney; On-site; AUD 120,000 - 180,000; Full time

Job summary

Kapital Consulting is seeking an experienced AI Engineer (Mid) to join as a foundational member to scale AI capabilities in the financial services domain.

Design, develop, test, and deploy ML models, build scalable pipelines, ensure regulatory compliance, and advance MLOps practices. Collaboration across teams to translate business problems into AI solutions.

Candidates should have experience with large-scale financial datasets, real-time streaming, and LLM architectures.

Qualifications

• Experience in funds & asset management or financial services.
• Knowledge of regulatory frameworks.
• Experience with large-scale financial datasets and real-time streaming data systems.
• Experience with OpenAI, Cursor, Claude, or similar tools.
• Strong understanding of LLM application architecture, including prompt engineering, tool calling, RAG, embeddings, vector databases, and agentic workflows.
• Experience developing RAG pipelines for document ingestion, chunking, retrieval, reranking, and context-aware generation.
• Passionate about building reliable, maintainable, measurable AI systems in production.

Responsibilities

• Design, develop, test, and deploy machine learning and deep learning models into production.
• Build scalable AI/ML pipelines and real-time inference systems.
• Ensure compliance with regulatory and governance requirements in financial services.
• Monitor, retrain, and improve deployed models for performance and fairness.
• Contribute to AI architecture, MLOps best practices, and cloud deployment strategies.
• Collaborate cross-functionally to translate business problems into AI-driven solutions.

Skills

ML/AI engineering

Production ML

MLOps

LLM architecture

Prompt engineering

Real-time streaming

Regulatory knowledge

Job description

Kapital Consulting is seeking an experienced AI Engineer (Mid) to join as a foundational member to scale AI capabilities in the financial services domain.

Design, develop, test, and deploy ML models, build scalable pipelines, ensure regulatory compliance, and advance MLOps practices. Collaboration across teams to translate business problems into AI solutions.

Candidates should have experience with large-scale financial datasets, real-time streaming, and LLM architectures.