Senior AI Engineer: Agentic Workflows & Data Quality

Intellias; Greater London; On-site; GBP 90,000 - 130,000; Full time

Job summary

Intellias is seeking an engineering lead to build production-grade agentic workflows that reason over research content, ingest diverse sources, and provide trusted outputs with provenance. The role focuses on the data foundations and guardrails needed to safely scale AI in investment management.

You will design and implement ingestion pipelines, evaluation harnesses, and observability tooling, working with stakeholders to move from vague briefs to concrete, automated quality standards in a

Qualifications

• Proven experience building production agentic and LLM systems across heterogeneous sources.
• Hands-on engineering of document ingestion and extraction pipelines with quality controls.
• Experience building evaluation guardrails: groundedness, provenance, and eval harnesses.
• Strong production Python engineering with CI/CD and automated testing.

Responsibilities

• Build agentic workflows that reason over research content and present policy-compliant outputs with confidence levels.
• Engineer automated quality checks on unstructured content prior to ingestion (empty content, truncation, OCR defects).
• Build vendor delivery validation to detect parsing/format defects and feed improvements back to data sourcing.
• Create evaluation harnesses, benchmarks, and guardrails for agent outputs with traceable provenance and citations.
• Develop dashboards and monitoring to surface data quality, confidence, and coverage gaps for engineers and PMs.
• Collaborate with platform engineering, data sourcing, and portfolio managers to translate business expectations into measurable automated standards.

Skills

Agentic workflows

Python engineering

Data ingestion pipelines

Evaluation guardrails

LLM observability

Multi-source reasoning

Tools

Snowflake

Linux/UNIX

Git

Jira

Job description

Intellias is seeking an engineering lead to build production-grade agentic workflows that reason over research content, ingest diverse sources, and provide trusted outputs with provenance. The role focuses on the data foundations and guardrails needed to safely scale AI in investment management.

You will design and implement ingestion pipelines, evaluation harnesses, and observability tooling, working with stakeholders to move from vague briefs to concrete, automated quality standards in a