Principal AI Engineer

Our client is a leading global investment management company headquartered in London. It manages over $228 billion in assets and serves institutional investors, pension funds, wealth managers, and other sophisticated clients worldwide. The firm specializes in quantitative investing, alternative investments, systematic trading strategies, and technology-driven asset management. Data science, machine learning, and AI are core components of its investment and research processes.

As part of our collaboration we will focus on two foundational capabilities required to enable safe and scalable AI adoption across the enterprise: Agentic Security and AI-Ready Data Foundations.

What project we have for you

We build the data foundations and evaluation frameworks that make AI useful, reliable and safe inside regulated financial firms. The value of an AI agent depends not only on the models behind it, but also on the quality of the structured and unstructured data it consumes and the accuracy, relevance and traceability of the outputs it produces. Your job is to measure that quality, identify where it breaks down and turn the findings into practical improvements.

This is an engineering role, not an analytical one. You will build the agentic workflows that reason over the firm's research content, and the ingestion, evaluation and guardrail tooling that makes their output trustworthy enough for investment professionals to act on. None of this tooling exists today - you would be building it from scratch.

What you will do

• Build agentic workflows that reason over research reports, transcripts, filings and news, and present portfolio managers with a clear view of what was found, what is missing, and how confident the system is in each answer.
• Engineer automated quality checks on unstructured source content before ingestion - empty or blank content, truncation, extraction fidelity, coverage gaps across expected document sets.
• Build vendor delivery validation : detect and quantify parsing and format defects in incoming feeds, feed them back to vendors and the data sourcing team, and fix extraction where it sits with us.
• Build evaluation harnesses, benchmarks and guardrails for agent output - groundedness, factual accuracy, relevance, and citation/provenance, so any claim can be traced back to a specific file and snippet.
• Ship monitoring and dashboards surfacing data-quality findings, confidence levels and coverage gaps to both engineering and PM audiences.
• Work directly with the platform engineering team, data sourcing, and portfolio managers to turn business expectations into measurable, automated quality standards.

What you need for this

• Proven experience in data wrangling, time-series analysis, statistical techniques, and data visualisation especially across structured and unstructured data.
• Building data quality pipelines and monitors for structured and unstructured datasets.
• Familiarity with vector databases, RAG, agent benchmarking and named entity recognition (NER)
• Contribute to AI-powered tools for investment teams - orchestration, RAG, agent benchmarking
• Proficient in the Python data science stack (Pandas, NumPy, Spark, Matplotlib) with experience leveraging AI-assisted coding tools to accelerate development.
• Working knowledge of Snowflake, Linux/UNIX, Git, Jira.
• Strong appetite for data curiosity, lineage and exploratory analysis.
• Passion for embracing agentic engineering - willingness and ability to work effectively with AI development tools as part of daily workflow.
• Ability to present technical results and concepts to non-technical audiences.
• Entrepreneurial mindset with a willingness to deeply understand investment strategies and proactively push data-driven solutions.
• Strong academic record with a degree in a STEM field.

Nice to have

• Previous experience working with investment professionals in a fast-paced environment preferred.
• Experience writing ETL pipelines and fluency in SQL preferred.