Forward Deployed Engineering (FDE) Architect (AI/ML)

Location: Remote from Poland, Albania, Bulgaria, Cyprus, Czech Republic, Estonia, Greece, Hungary, Italy, Lithuania, Macedonia, Malta, Moldova, Portugal, Romania, Serbia, Slovakia, Slovenia, Spain, or Ukraine.

Employment type: Contract.

Company

hirify.global delivers AI engineering solutions, including a credit analytics platform for a regulated financial-services business operating in the UK and Ireland.

What you will do

• Translate business problems into architecture, delivery plans, acceptance criteria, and actionable requirements.
• Design the transition from working models and dashboards to a production-ready ML platform supporting multiple portfolios and clients.
• Build reproducible workflows for data preparation, feature engineering, training, validation, scoring, and deployment, with versioning for code, data, models, and calibration logic.
• Work with the modelling lead on probability of default, prepayment, lifetime risk, and loss estimates, keeping definitions consistent across models, tables, and dashboards.
• Strengthen data quality, traceability, testing, privacy, access control, auditability, model governance, monitoring, rollback, and operational documentation.
• Lead technical discussions, review code, unblock engineers, communicate risks and decisions to the client, and assess useful applications of natural-language or LLM-assisted analytics.

Requirements

• Track record of designing and delivering production data or ML systems, with ownership beyond prototypes and architecture diagrams.
• Strong hands-on Python and SQL, including dataset investigation, modelling-code review, and implementation of critical solution components.
• Practical MLOps experience with reproducible pipelines, model and data versioning, testing, deployment, monitoring, and managing model updates operationally.
• Experience with cloud data platforms and modern warehouses; the ability to reason about data access, performance, cost, and deployment constraints.
• Understanding of model evaluation, calibration, data leakage, changing populations, and the difference between model quality and system reliability.
• Ability to work through incomplete documentation and inconsistent business definitions and turn findings into maintainable code and clear documentation.
• Strong client-facing English communication, including explaining trade-offs to non-technical stakeholders, challenging requirements constructively, and taking responsibility for decisions.
• Experience leading a small technical team while remaining actively involved in implementation.

Nice to have

• Credit-risk, lending, or servicing analytics experience, including PD, LGD, ECL, prepayment, or portfolio stress testing.
• Regulated financial-services delivery and model-governance experience.
• Experience with Streamlit, Azure ML, semantic data layers, or analytics applications built on Snowflake.
• Experience building configurable ML products serving multiple portfolios or clients.
• Experience evaluating natural-language analytics, text-to-SQL, or LLM-assisted workflows.

Culture & Benefits

• Remote contract work from the listed European countries.
• Hands-on collaboration with client business and technology teams, a credit-domain and modelling lead, and other engineers.
• Architecture evolves according to product needs rather than a predetermined stack.
• Technology environment includes Snowflake, SQL, Python, Streamlit, Azure ML, and AI-assisted development tools.

Hiring process

• Discuss a production ML or analytics system personally built, including owned architecture decisions and code.
• Explain how to adapt a model and calibration for a new portfolio while preserving reproducibility, access boundaries, and validation.
• Describe a situation where a business or technical assumption was challenged, an alternative proposed, and a better outcome delivered.