- Lead end-to-end development of AI features and services (LLM, NLP, CV, predictive ML) from prototyping to production, aligned to clear product outcomes and KPIs.
- Review and govern AI solution designs to ensure correctness, robustness, security, and responsible-AI compliance (bias, toxicity, hallucination risk, data leakage).
- Partner with product and engineering teams to translate business problems into AI solution approaches (build vs buy, model choice, data needs, evaluation strategy) and executable delivery plans.
- Lead and scale high-performing AI engineering teams delivering multiple initiatives in parallel (AI-enabled applications, internal platforms, and developer tooling).
- Identify AI-specific risks (data quality, drift, adversarial use, hallucinations, latency/cost) and implement mitigation strategies throughout the delivery lifecycle.
- Promote automation and engineering excellence (CI/CD for ML, automated evaluation, red-teaming, monitoring/alerting, and cost/performance optimization) to improve quality and velocity.
- Collaborate closely with product, data, security, legal/compliance, and global engineering teams to align on AI use cases, constraints, and delivery expectations.
- Coordinate architecture and delivery plans across multiple parallel AI initiatives (e.g., GenAI assistants, search/RAG, personalization, fraud/risk models, automation).
- Work with platform teams to ensure reusable AI components and consistent patterns (prompt orchestration, retrieval, model gateways, feature management, observability) across the portfolio.
- Act as a trusted technical advisor on AI strategy, platform choices, vendor/partner decisions, and responsible AI governance.
- Explore, evaluate, and introduce AI technologies where they add clear business value (models, frameworks, orchestration tools, data platforms), balancing performance, cost, and risk.
- Continuously improve AI development lifecycle practices (data/versioning, evaluation, deployment, monitoring, incident response) and governance models.
- Mentor AI engineers and applied scientists, strengthening engineering rigor, model quality, and responsible-AI maturity.
- Strong sense of ownership and accountability for AI outcomes, production reliability, and responsible-AI risk management.
- Ability to think strategically about AI adoption while remaining hands-on when needed (design reviews, critical debugging, and technical decision making).
- Pragmatic problem-solving skills with a focus on shipping AI capabilities safely, iterating quickly with evidence, and delivering business value.
- A collaborative mindset and the ability to influence across product, engineering, data, and risk/compliance stakeholders without relying on formal authority.
- Bachelor’s degree or above in Computer Science, Machine Learning, Data Science, or a related field (or equivalent practical experience).
- 8+ years of experience in software engineering, including significant leadership experience delivering AI/ML systems in production at scale.
- Strong software engineering fundamentals (distributed systems, APIs, data structures) and proficiency with modern AI development stacks (Python plus one of Java/Go/C++), including strong code quality practices.
- Proven experience building and operating AI/ML workloads on public cloud platforms (AWS/Azure/GCP), including scalable model training and low-latency inference deployment.
- Solid experience with data platforms and pipelines (e.g., Databricks, Snowflake, Spark) and familiarity with vector search / embeddings for retrieval-based AI solutions.
- Deep understanding of modern engineering practices and MLOps (CI/CD, testing, experiment tracking, model/version management, monitoring, incident management).
- Experience working in global or distributed teams across different regions and time zones.
- Strong communication skills in English and Chinese, including the ability to present complex AI topics and decisions to senior stakeholders.
- Demonstrated experience with Responsible AI practices and governance (privacy, security, bias/fairness, content safety, auditability, and regulatory compliance).
- Hands-on experience with LLM/GenAI application development (prompting, RAG, Agents, tool/function calling), model evaluation, and production observability.
Across the globe, institutional investors rely on us to help them manage risk, respond to challenges, and drive performance and profitability. We keep our clients at the heart of everything we do, and smart, engaged employees are essential to our continued success.
We are committed to fostering an environment where every employee feels valued and empowered to reach their full potential. As an essential partner in our shared success, you’ll benefit from inclusive development opportunities, flexible work-life support, paid volunteer days, and vibrant employee networks that keep you connected to what matters most. Join us in shaping the future.
As an Equal Opportunity Employer, we consider all qualified applicants for all positions without regard to race, creed, color, religion, national origin, ancestry, ethnicity, age, disability, genetic information, sex, sexual orientation, gender identity or expression, citizenship, marital status, domestic partnership or civil union status, familial status, military and veteran status, and other characteristics protected by applicable law.
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