Forward Deployed Engineering (FDE) Architect - Credit Analytics Platform

We are looking for a hands-on Forward Deployed Engineering (FDE) Architect to help develop a credit analytics platform for a regulated financial-services business operating in the UK and Ireland.

The platform brings together lending and servicing data to support portfolio analysis, credit-risk modelling, and better business decisions. Current work covers probability of default, redemption and prepayment, expected credit loss, and interactive analytics. The next challenge is to turn this work into a reliable, maintainable platform that can support different portfolios and client needs.

You will work directly with the client’s business and technology teams, alongside a credit-domain and modelling lead and other engineers. You will own the engineering architecture, contribute production code, and help the team decide what to build and how to deliver it. We need someone who can question assumptions, propose simpler alternatives, and explain technical trade-offs clearly.

What you will do

• Translate business problems into an architecture, practical delivery plan, and clear acceptance criteria. Identify gaps in requirements, data, and dependencies before they become delivery problems.

• Design the path from working models and dashboards to a production-ready ML platform. Make deliberate choices about shared components, portfolio-specific configuration, model calibration, and future client onboarding.

• Build and improve reproducible workflows for data preparation, feature engineering, training, validation, scoring, and deployment. Establish clear versioning for code, data, models, and calibration logic.

• Work with the modelling lead on how probability of default, prepayment, lifetime risk, and loss estimates are calculated, tested, and exposed to applications. Keep business definitions consistent across models, tables, and dashboards.

• Strengthen the data foundation: trace results to source, investigate data-quality issues, document assumptions, and build automated checks around important transformations.

• Improve the reliability of the Snowflake-backed analytics application, including deployment, testing, permissions, and integration with model outputs.

• Put appropriate MLOps practices in place, including repeatable releases, monitoring, failure handling, rollback, and operational documentation.

• Design for privacy, access control, auditability, and model governance. Work with domain experts to make validation, stability, and fairness checks practical and reviewable.

• Lead technical discussions, review code, unblock engineers, and keep the client informed about progress, risks, and architectural decisions.

• Assess where natural-language or LLM-assisted analytics would add value, without losing focus on trustworthy data, robust models, and useful products.

What we are looking for

• A track record of designing and delivering production data or ML systems, with ownership extending beyond prototypes and architecture diagrams.

• Strong hands-on Python and SQL. You should be comfortable investigating a dataset, reviewing modelling code, and implementing critical parts of the solution yourself.

• MLOps experience: reproducible pipelines, model and data versioning, testing, deployment, monitoring, and the operational consequences of model updates.

• Experience with cloud data platforms and modern warehouses. Snowflake experience is particularly relevant; you should be able to reason about data access, performance, cost, and deployment constraints.

• An understanding of model evaluation, calibration, data leakage, changing populations, and the difference between model quality and system reliability.

• The ability to work through incomplete documentation and inconsistent business definitions, and turn findings into maintainable code and clear documentation.

• Strong client-facing communication in English. You can explain trade-offs to non-technical stakeholders, constructively challenge a requirement, and take responsibility for the resulting decision.

• Experience leading a small technical team while remaining actively involved in implementation.

Useful additional experience

• Credit-risk, lending or servicing analytics, including PD, LGD, ECL, prepayment, or portfolio stress testing.

• Regulated financial-services delivery and model governance.

• Streamlit, Azure ML, semantic data layers, or analytics applications built on Snowflake.

• Configurable ML products serving multiple portfolios or clients.

• Evaluated natural-language analytics, text-to-SQL, or LLM-assisted workflows.

Current technical environment

Snowflake, SQL, Python, Streamlit, and an Azure ML environment. The team also uses AI-assisted development tools. You will help evolve the architecture based on the product’s needs rather than introduce a predetermined stack.