ai engineer in fintech

The client is a 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, with data science, machine learning, and AI integrated into its investment and research processes.

• Build agentic workflows that reason over research reports, transcripts, filings and news, presenting portfolio managers with findings, missing information and confidence levels;
• Engineer automated quality checks for unstructured source content before ingestion, including blank content, truncation, extraction fidelity and coverage gaps;
• Build vendor delivery validation to detect and quantify parsing and format defects, provide feedback to vendors and the data sourcing team, and fix extraction issues handled internally;
• Build evaluation harnesses, benchmarks and guardrails for agent output, including groundedness, factual accuracy, relevance and citation/provenance;
• Ship monitoring and dashboards showing data-quality findings, confidence levels and coverage gaps to engineering and portfolio management audiences;
• Work directly with platform engineering, data sourcing and portfolio managers to turn business expectations into measurable, automated quality standards.

• Proven experience building production agentic and LLM systems with multi-agent or orchestrated workflows across heterogeneous sources;
• Experience surfacing confidence, gaps and provenance to end users;
• Hands-on experience engineering document ingestion and extraction pipelines, including parsing, chunking and automated quality controls;
• Experience detecting empty or truncated content, incorrect document sections, duplication, encoding defects and OCR defects;
• Experience building evaluation and guardrail infrastructure for AI systems, including groundedness scoring, citation and provenance, evaluation harnesses, regression suites and LLM observability;
• Strong production Python engineering experience with unattended services and pipelines, testing, CI and code standards;
• Ability to shape problems from deliberately vague briefs with business stakeholders;
• Ability to explain technical results to non-technical audiences;
• Nice to have: retrieval quality tuning, chunking strategy, embedding choice, retrieval evaluation, structured and time-series data-quality experience, ETL pipelines, SQL, experience working with investment professionals in a fast-paced environment, Snowflake, Linux/UNIX, Git, Jira.

• The role is based in Poland, Spain or the United Kingdom;
• Intellias provides benefits supporting well-being and professional growth;
• Intellias is an equal opportunity employer.