AI Engineer

Key Responsibilities AI & Agentic Workflow Development: Design and implement cutting-edge AI agents and Large Language Models (LLM) utilizing Python 3.11, the Groq API, and Llama 3.3 70B. Build robust Retrieval-Augmented Generation (RAG) pipelines while ensuring strict data privacy through PII masking and PDPA compliance.

Banking & Regulatory Alignment: Analyze and bridge business needs across Core Banking Systems, loan processing, risk management, KYC, and AML, aligning all solutions with Monetary Authority of Singapore (MAS) regulations, MAS FEAT principles, and MAS AI Risk Management guidelines.

Business Analysis & Documentation: Translate complex business objectives into clear documentation including Business Requirements Documents (BRD), Functional Requirements Specifications (FRS), Functional Specification Documents (FSD), and user stories. Manage project tracking using JIRA, Confluence, and Microsoft Visio.

Technical Integration: Collaborate with engineering teams using SQL, REST APIs, JSON, microservices, cloud computing environments, MockAPI.io, Git, and VS Code.

Agile Delivery & Stakeholder Management: Drive delivery through Agile and Scrum methodologies, handling sprint planning, UAT management, stakeholder engagement, vendor coordination, and change management.

Qualifications & Technical Stack AI/LLM Stack: Python 3.11, AI Agent Development, LLMs, Prompt Engineering, Groq API, Llama 3.3 70B, RAG, PII Masking, PDPA Compliance.

Domain & Compliance: Core Banking, Loan Processing, Risk & Finance, KYC, AML, MAS Regulations, MAS FEAT Principles, PDPA, MAS AI Risk Management.

Business Analysis: BRD, FRS, FSD, User Stories, Process Mapping, Gap Analysis, JIRA, Confluence, Visio.

Technical Tools: SQL, REST APIs, JSON, Microservices, Cloud Computing, MockAPI.io, Git, VS Code.