Engineer- Deployment & Engineering for Agentic AI Solutions (Financial Services), IHPC

About UsAt 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.RequirementsMust-Have QualificationsBachelor’s or Master’s degree in Computer Science, Software Engineering, AI/ML, or related fieldStrong hands-on experience in Python and modern ML frameworks (e.g., PyTorch)Experience deploying LLM-based or AI systems into production environmentsExperience with MLOps pipelines, model monitoring, and observability toolsSolid understanding of software engineering best practices (version control, CI/CD, testing, code reviews)Familiarity with cloud platforms (e.g., AWS, Azure)Strong-Plus QualificationsKnowledge of data security, privacy, and compliance requirements (especially in financial services)Familiarity with financial data pipelines, structured data systems, or regulatory workflowsKey ResponsibilitiesCollaborate with AI Scientists to translate research prototypes into scalable production systemsMaintain deployment pipelines for agentic AI and LLM-based solutionsOptimize system performance, latency, reliability, and cost efficiencyImplement monitoring, logging, evaluation, and alerting mechanisms for deployed AI systemsEnsure compliance with enterprise-grade security, privacy, and governance standardsSupport testing, validation, and user acceptance processes with industry partnersContribute to documentation, system architecture design, and deployment best practices