AI/ML Engineer

Future Works

AI/ML Engineer designing and building AI agent systems for complex transactions. Collaborating with cross-functional teams and ensuring quality through end-to-end testing.

Posted 7/9/2026contractRemote • 🇬🇧 United KingdomMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in building agent capabilities and data pipelines, with strong proficiency in Python and document processing. Familiarity with financial analytics and NLP techniques is essential for success in a document-heavy transaction environment.

Highest-signal resume keywords

Python ProgrammingData Pipeline DevelopmentAgent FrameworksFinancial AnalyticsNLP Information Extraction

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Software EngineeringMachine Learning EngineeringDocument ProcessingClause ExtractionEntity ExtractionTemplate PopulationMarket-Data QueryFinancial AnalyticsScenario ModellingData Integration

Soft Skills

Agile ExecutionHypothesis-Driven Approach

Tools & Technologies

LLM Code AssistantsAgentic Engineering

Industry Keywords

Commercial Real EstateDocument-Heavy TransactionsEnd-to-End TestingQuality AssuranceWorkflow Stages

Tech Stack

Tools & technologies

AssemblyPython

About the role

Key responsibilities & impact

Per-Stage Agent Build: Implement agent capabilities for individual workflow stages (for example engagement agreement generation, needs-assessment brief, market survey and long-list scoring, tour management, RFP and proposal analytics, lease abstraction, and close-out and invoicing), building to the orchestration model and stage handoff protocols defined by the Lead Architect.
Tool & Integration Development: Build the typed tools the agents call: template population, market-data query, financial analytics (NER, TI, free rent, NPV), clause and lease-clause extraction, and document assembly.
Data Pipelines: Build and maintain pipelines against sandbox and representative sample data, including the property availability data source.
Human-in-the-Loop Implementation: Encode the approval gates and review checkpoints specified by the architect, so binding steps always route through a broker confirmation.
Quality & End-to-End Testing: Validate components against deal-scenario data and participate in end-to-end testing across all eight stages, checking artifact quality and stage handoff integrity.
Build-Phase Readiness: Participate in Phase 1 architecture workshops to ensure the build starts on solid foundations.

Requirements

What you’ll need

4+ years in software or machine learning engineering, with production experience building LLM-powered features or agents.
Agent Tooling: Hands-on experience with agent frameworks, tool and function calling, retrieval, and prompt engineering.
Python & Data Engineering: Strong Python and practical experience building data pipelines and integrating structured and unstructured data sources.
Document Processing: Comfort parsing, extracting, and generating documents across formats such as PDF, DOCX, and structured templates.
Financial & NLP Depth: Experience implementing financial analytics (lease economics, NPV, scenario modelling) and NLP information extraction (clause and entity extraction from contracts or leases) is strongly preferred.
Domain Exposure: Exposure to commercial real estate or another document-heavy transaction domain is a plus.
AI-Native Workflow: Comfort utilizing LLM code assistants and agentic engineering to ship faster and with higher quality.
Agile Execution: Ability to move quickly in hypothesis-driven, milestone-based sprint cycles, shipping working components and iterating against real evidence.

Benefits

Comp & perks

Work from anywhere, forever - We are a fully remote and global team. We trust you to manage your time and energy to deliver exceptional results.
Connect deeply - We gather for immersive, all-expenses-paid company retreats in unique locations to connect, learn, and grow together.
Share in the upside - A competitive compensation package including equity, bonuses, substantial participation in company profits with a clear growth path to C-Level leadership based on performance.