AI Engineer (Staff/Principal) New York

What matters most

• Own capabilities through customer adoption

  The role owns core product capabilities from customer conversations and architecture through implementation, deployment and adoption. Prepare to explain how you translate customer needs into a scoped technical solution and assess its business impact.

• Lead document-focused AI features

  The posting names three development areas: lease extraction at scale, planning-document analysis and advice drafting, and multi-step generation of market research reports. Prepare to discuss design choices for these distinct workflows rather than treating them as one generic AI feature.

• Build scalable customer-facing integrations

  Fifth Dimension seeks scalable AI solutions and a track record of shipping critical-path Python features and maintaining high-quality APIs and integrations. Be ready to explain architectural and interface decisions for enterprise customers.

• Build in privacy and security

  Implementing data privacy and security by design is an explicit duty for work involving customer documents. Prepare to show how those concerns shape a solution from architecture through deployment, rather than being added at the end.

• Set engineering direction while delivering

  At Staff/Principal level, the role mentors engineers, helps establish best practices, uses AI coding tools to accelerate development, and values data-backed technical decisions and clear communication. Prepare examples of how technical leadership supports delivery.

Likely challenges to think through

• Shipping quickly without locking in poor architecture

  A likely trade-off, not a confirmed company problem: immediate customer value may compete with scalable design in a fast-paced environment. The posting explicitly calls for balancing user needs with long-term architecture. Prepare a concrete decision framework for scoping a first release, timeboxing uncertain approaches and identifying what should be revisited.

• Making complex AI outputs dependable

  A plausible difficulty is maintaining useful, trustworthy results across varied leases, planning documents and multi-step report generation. Prepare to discuss how you would check extraction and generated outputs, investigate unexpected model behavior and communicate limitations; the posting values methodical investigation and intellectual honesty.

• Fitting AI features into enterprise systems securely

  A plausible challenge is reconciling a customer's integration needs with robust APIs, scale, privacy and security. The posting requires integrations and security by design, but does not specify customer architectures. Prepare to explain how you would establish interface requirements and surface those trade-offs with customer and commercial teams.

Topics to prepare

• Scalable AI architecture and trade-offs

  The posting calls for scalable enterprise AI solutions and decisions that balance immediate customer needs with long-term architecture. Exercise: sketch two designs for a lease-extraction capability and explain their scaling, maintenance, and delivery trade-offs.

• Document extraction at scale

  Leading extraction systems for property leases is an explicit responsibility. Exercise: define the fields, source evidence, error cases, and quality measures you would use to assess extraction from a varied set of sample leases.

• Planning workflows and agentic generation

  The role explicitly covers planning-document analysis, drafting advice, and multi-step generation of market research reports. Exercise: map the stages of each workflow, noting where source review, human judgment, and failure handling belong.

• Privacy and security by design

  The posting explicitly requires privacy and security in solutions. Exercise: review a proposed enterprise document flow and identify where access controls, data handling decisions, and integration boundaries need attention.

• Product ownership and customer value

  The engineer owns capabilities from customer conversations through deployment and adoption. Exercise: turn a hypothetical customer request into a scoped feature, success measure, and explanation of what you would defer.

Skills to revise

• Production-oriented Python

  Strong Python skills and shipping customer-facing features are explicit expectations. Exercise: implement a small document-processing component with clear interfaces, error handling, and focused tests; explain how you would maintain it on a critical path.

• API and integration design

  The posting requires high-quality APIs and integrations, including work with enterprise customers. Exercise: draft an API contract for submitting a lease and retrieving extracted data, then walk through errors and integration troubleshooting.

• Lease-extraction prototype

  Property-lease extraction is a named feature area. As a preparation exercise, build a small prototype using synthetic lease excerpts, compare extracted fields with a hand-checked answer set, and explain discrepancies; this exercise is not a stated employer requirement.

• Multi-step workflow implementation

  Workflow automation and agentic report generation are explicit responsibilities. As a preparation exercise, implement a minimal staged flow using sample inputs, with visible intermediate outputs and a way to stop or revise a faulty draft.

• AI-assisted coding with review

  Proficiency with tools such as Claude Code, Cursor, or GitHub Copilot is explicitly requested. Exercise: use one of those tools for a bounded Python change, then independently review the resulting code, tests, and architectural implications.

• Technical communication across teams

  The role works with customers, product, and commercial teams and mentors engineers. Exercise: explain one proposed feature twice—first in terms of customer value and scope, then as an engineering design with trade-offs and open questions.

• Prepare an end-to-end project walkthrough

  Select a relevant customer-facing Python feature, if available. Outline the customer need, architecture, implementation, deployment, adoption, measurable impact and tradeoffs; the role owns this full lifecycle.

• Sketch a lease-extraction design

  As practice for the advertised work, draw a scalable pipeline for extracting data from property leases. Show how you would check extraction quality, handle difficult documents and protect customer data; these are proposed design choices, not a specified interview task.

• Rehearse a multi-step AI workflow

  Outline how you would analyze planning documents and draft advice, or generate a market research report in multiple steps. Identify where evidence is checked and where a person should review output.

• Prepare an API and integration example

  Bring a concise account of an API or integration you built or maintained, if available. Be ready to explain its Python implementation, enterprise integration constraints, security decisions and how you resolved failures.

• Show how you work with AI coding tools

  Prepare a concrete example using Cursor, GitHub Copilot or Claude Code, if available. Explain what the tool accelerated, what you verified yourself and how you kept architectural decisions under your control.

• Collect staff-level decision and mentoring stories

  Choose examples, if available, of balancing an urgent customer need against long-term architecture, collaborating with commercial or product colleagues, and mentoring engineers. Note the evidence behind each decision and what you would change.

• Write questions for the interviewers

  Ask the CTO how customer conversations become engineering priorities and how adoption is assessed. Ask how the team handles privacy in document workflows, evaluates AI reliability and divides ownership of core product components.