You are a strategic thinker passionate about driving solutions in “Applied AI & Machine Learning”. You have found the right team.
As “Applied AI & Machine Learning Professional” within our “Asset Management Data Science Team ” , you will be responsible for setting and improving our organizational objectives, and ensuring their consistent accomplishment.
Job Responsibilities
- Design and implement scalable architecture for LLM-powered investment tools, including reusable patterns for integration, governance, observability, and access control
- Build and scale AI applications and automated workflows using models, retrieval, and tools with orchestration for state management, retries, checkpointing, deterministic controls, and appropriate human oversight
- Develop context-management and retrieval-augmented generation (RAG) capabilities, including ingestion, chunking, metadata, embeddings, hybrid search, reranking, and grounded outputs
- Create reliable data and knowledge pipelines that transform structured and unstructured enterprise content into governed, high-quality inputs for AI applications
- Establish engineering standards for reusable tools and model integrations, including interfaces, permissions, testing, failure handling, and documentation
- Implement evaluation and end-to-end observability for AI systems, including structured logging and tracing across model calls, retrieval steps, and tool execution
- Optimize AI systems for quality, task completion, latency, token usage, cost, reliability, and business impact
- Partner with portfolio managers and research teams to understand investment processes and translate them into practical solutions within the firm’s ecosystem
Required qualifications, capabilities and skills
- Earn a Master’s degree or PhD in computer science, statistics, mathematics, engineering, econometrics, or another quantitative field
- Apply a strong foundation in statistics, probability, experimental design, and machine learning to select appropriate methods and interpret quantitative results
- Build, deploy, and scale LLM-powered applications, including retrieval, tool use, workflow orchestration, state management, structured outputs, and evaluation, using frameworks such as LangGraph, Semantic Kernel, LlamaIndex, or equivalent
- Design prompts and context effectively, including embeddings, vector and keyword search, reranking, model selection, and quality/latency/cost optimization
- Write strong Python and SQL, using common data and machine learning libraries and frameworks
- Demonstrate sound numerical intuition and understanding of financial markets, investment research, portfolio construction, risk, performance, and investment data
- Translate ambiguous business requirements into scalable technical solutions and communicate technical trade-offs to a range of stakeholders
Preferred qualifications, capabilities and skills
- Deliver generative AI or machine learning solutions in a regulated enterprise environment
- Bring front-office or buy-side experience, particularly in investment research, portfolio analytics, performance attribution, or decision analytics
- Incorporate unstructured or alternative data into research and production workflows
- Apply familiarity with Model Context Protocol (MCP) or comparable standards for securely connecting AI applications to enterprise data, tools, and services
- Use LLM observability and evaluation standards or platforms such as OpenTelemetry, OpenInference, Arize Phoenix, LangSmith, or equivalent (including traces across model, retrieval, and tool-execution steps)
- Leverage familiarity with knowledge graphs, multimodal models, fine-tuning, synthetic data, or advanced model-evaluation techniques
- Pursue CFA qualification or progress toward the CFA designation