Artificial Intelligence Engineer(AI)

Title:- AI Engineer

Location:- Sydney/Melbourne

Duration:- Perm Role

Salary- As per market standards

Skills required:

- 5+ years' experience in Data engineering, Machine Learning with proven experience in delivery of large or medium scale production systems.

- 2+ years' experience in Capital markets / Trading & Risk domain as a developer.

- At least 1 year experience working with AI Tools like Cursor, Claude building custom agents preferably for a banking domain.

- Strong experience with Python, Java, or Scala in enterprise environments, building APIs/services (REST/Google RPC), event-driven patterns, and working with large-scale data

- Practical GenAI experience in RAG architecture, vector databases, prompt engineering, evaluation frameworks, and safe and secure agent design.

- Working knowledge with cloud platforms (AWS/Azure/GCP) and container based deployments using Docker, Kubernetes

- Experience with CI/CD, Git, and automated testing frameworks.

- Knowledge of NLP, embeddings, transformers, and model fine tuning.

- Strong data engineering skills: SQL, ETL, data modelling, feature engineering.

- Experience in working with risk SMEs, quants, and technology teams, Strong documentation and communication skills.

- Ability to operate in regulated, high control environments, Problem solving mindset with a focus on automation and simplification

Nice To Have:

- Experience building chatbots, agents, or intelligent assistants.

- Familiarity with leading Trading and Risk platforms such as Murex, Calypso, or FIS

- Understanding of Market risk measures (FRTB), Credit Risk (CCR), Various Greeks, P&L explain.

- Experience with regulatory reporting systems or risk engines.

- Certifications in cloud, AI/ML, or financial risk FRM, CFA, CQF

Responsibilities:

- Build Risk domain specific AI agents that leverage internal knowledge bases (Confluence, functional/technical specs, solution design documents, runbooks, regulatory guidelines).

- Implement RAG pipelines: document ingestion, chunking, embeddings, retrieval evaluation, citations/traceability, and freshness/versioning across knowledge bases.

- Develop risk-focused Natural Language Processer (NLP) to interpret regulatory text (e.g., FRTB, SA-CCR, IRRBB) into implementable technology requirements; support impact analysis and change delivery.

- Translate regulatory frameworks like Basel III / IV into interpretable logic for agents, build models that assist in interpreting risk sensitivities, capital calculations, VaR/MRA outputs, and limit breaches.

- Implement conversational interfaces and chatbots for risk users, support teams, and developers.

- Develop APIs and microservices to expose AI capabilities to risk platforms, downstream systems and trading platforms (Murex/Calypso/FIS) and surrounding ecosystem like Market Data, Staic and Reference data systems.

- Own end-to-end delivery of GenAI products: discovery → architecture → build → testing → production rollout → monitoring and continuous improvement.

- Coordinate with Bank's AI Governance team to Establish / Follow LLM standards across prompt management, version control, offline/online evaluation, red-teaming, model monitoring, cost/latency optimization.

- Implement guardrails to prevent hallucinations, data privacy, and regulatory compliance

- Lead code reviews and uplift engineering quality (testing strategy, secure coding, performance, reliability), including AI-assisted code review patterns and guardrails.

- Mentor new joiners, prepare onboarding / SOP documents, contribute to AI community practice on reusable libraries/patterns.