Job Description: Engineer– Deployment & Engineering for Agentic AI Solutions (Financial Services)

About Us

At A*STAR Institute of High Performance Computing (A*STAR IHPC), our team conducts advanced AI research to solve real-world challenges in financial services. We focus on developing agentic AI systems, large language models (LLMs), and foundation models that enable complex reasoning, and automation across financial workflows.

Our work bridges cutting-edge AI research and practical financial applications, including intelligent document analysis, regulatory compliance, risk reasoning, and decision support. We collaborate closely with fintech companies and financial institutions to translate research innovations into deployable systems.

We are seeking an Engineer to work closely with AI Scientists to operationalize and deploy agentic AI solutions into real-world financial environments. You will be responsible for engineering, integrating, and deploying agent-based AI systems into secure, scalable, and production-grade infrastructures. You will work closely with AI Scientists, domain experts, and industry partners to ensure research innovations are effectively implemented and delivered in production settings.

Requirements

Must-Have Qualifications

• Bachelor’s or Master’s degree in Computer Science, Software Engineering, AI/ML, or related field
• Strong hands-on experience in Python and modern ML frameworks (e.g., PyTorch)
• Experience deploying LLM-based or AI systems into production environments
• Experience with MLOps pipelines, model monitoring, and observability tools
• Solid understanding of software engineering best practices (version control, CI/CD, testing, code reviews)
• Familiarity with cloud platforms (e.g., AWS, Azure)

Strong-Plus Qualifications

• Knowledge of data security, privacy, and compliance requirements (especially in financial services)
• Familiarity with financial data pipelines, structured data systems, or regulatory workflows

Key Responsibilities

• Collaborate with AI Scientists to translate research prototypes into scalable production systems
• Maintain deployment pipelines for agentic AI and LLM-based solutions
• Optimize system performance, latency, reliability, and cost efficiency
• Implement monitoring, logging, evaluation, and alerting mechanisms for deployed AI systems
• Ensure compliance with enterprise-grade security, privacy, and governance standards
• Support testing, validation, and user acceptance processes with industry partners
• Contribute to documentation, system architecture design, and deployment best practices