About the AI Engineer Role at Socure

Socure is building identity trust infrastructure for the digital economy, verifying good identities in real time and stopping fraud before it starts. This particular role (posted at the Staff level within a new Internal AI Engineering team, reporting to the Head of New Product Engineering) is about building agentic workflows and internal productivity tools that reshape how Socure operates across sales, marketing, revenue ops, finance, talent, and legal. You'll be the first major internal partner building on Socure's Agentic AI Foundations platform, using its primitives (agent runtime, orchestration, memory, tool registry, guardrails, observability) to ship faster while shaping its roadmap.

Socure wants an owner who defines what to build, not just executes a spec. The posting is explicit: this is not a place for predictability or narrow scope. You'll partner directly with non-technical business teams to catalog manual, high-frequency workflows, then design multi-step, tool-augmented agents that plan, execute, observe, and improve. Expect a strong emphasis on production reliability, evaluation, and human-in-the-loop controls.

Quick Stats

* Typical process: 4 to 5 rounds, roughly 3 to 5 weeks end to end

* Format: Recruiter phone screen, then video technical and behavioral rounds (Hybrid - US)

* Core focus: LLM APIs and agentic frameworks, Python backend/APIs, agent architecture (planner-executor, reflection), evaluation and observability, stakeholder collaboration, operating in ambiguity

* Difficulty: Hard, because it is a Staff-level role that combines deep production AI/agent engineering with the ability to scope undefined problems and partner with business teams

What Socure Looks For

* 8+ years of software engineering with at least 2 years on AI/ML or LLM-powered systems in production

* Deep hands-on experience with LLM APIs and at least one agentic framework (LangGraph, CrewAI, or AutoGen)

* Strong Python and proven ability to build production backend services, APIs, and data pipelines

* Proven ability to operate in ambiguity and ship AI/automation that directly impacts business operations

Round 1: Recruiter Screen (~30 minutes)

What to Expect

A recruiter or talent partner opens with your background, motivation, and fit for a high-ownership, fast-moving environment. Expect questions about your years of software and AI/ML experience, the agentic work you've shipped, why Socure's identity mission appeals to you, and whether you're comfortable with a role that has broad scope and undefined problem spaces. They will also confirm logistics (Hybrid - US) and touch on the compensation range ($220K to $290K plus equity and bonus).

Example Questions

* "Walk me through your background and the AI or LLM systems you've shipped to production."

* "Why Socure, and why this internal AI engineering team specifically?"

* "Tell me about a time you walked into an undefined problem and figured out what to build."

* "What are you looking for in terms of scope, ownership, and compensation?"

Tips

* Have a crisp 60-second pitch that connects your 8+ years of engineering and 2+ years of production AI/LLM work to this team's agentic mandate.

* Show ownership energy: mention a workflow you automated end to end and the measurable business impact it drove.

* Rehearse this quick, high-signal conversation with Nora AI's Standard Mode so your pitch and motivation answers land tightly.

Round 2: Technical Deep Dive on Agentic Systems (~60 minutes)

What to Expect

This round targets the heart of the role: designing and building production agent workflows. Expect to discuss LLM APIs, agentic frameworks (LangGraph, CrewAI, AutoGen), planner-executor and reflection-based architectures, stateful agents, and human-in-the-loop controls like approval gates, fallback paths, and escalation. You may work through a real design problem or walk deeply through an agent system you shipped, with the interviewer probing reliability, tool use, and failure handling.

Example Questions

* "Design a multi-step agent that automates a Revenue Ops workflow. How does it plan, execute, observe, and iterate?"

* "Compare planner-executor and reflection-based architectures. When would you reach for each?"

* "How do you build stateful agents with human-in-the-loop approval gates, fallback paths, and escalation?"

* "How do you handle tool calling, structured tool integration, or MCP in a production agent?"

Tips

* Anchor answers in real production experience with at least one agentic framework, not just prototypes or demos.

* Emphasize reliability, observability, and maintainability by design, since the posting calls out systems thinking as a core requirement.

* Drill these architecture and framework questions with Nora AI's Technical Mode so you can explain planner-executor, reflection, and human-in-the-loop patterns fluently out loud.

Round 3: Python, Backend, and Evaluation (~60 minutes)

What to Expect

A hands-on engineering round focused on strong Python, production-grade backend services, APIs, and data pipelines, plus how you evaluate and optimize AI systems. Expect coding or code-review discussion and questions about evaluation harnesses, tracing, failure-mode analysis, and accuracy versus speed tradeoffs. Preferred topics like RAG pipelines, vector databases, memory/grounding, evaluation frameworks (LangSmith, Weights & Biases, Arize), and AWS LLM infrastructure (Bedrock, AgentCore/Strands, Lambda, SageMaker) may come up.

Example Questions

* "Write or walk through a Python service that exposes an agent workflow behind an API. How do you structure it for reliability?"

* "How do you build an evaluation pipeline for an agent: accuracy benchmarking, failure-mode analysis, and regression tracking?"

* "Describe a RAG or memory/grounding system you built. How did you manage retrieval quality and hallucinations?"

* "How would you optimize an agentic solution for both speed and accuracy in production?"

Tips

* Be ready to write clean, production-minded Python and talk through error handling, observability, and data pipeline design.

* Bring concrete metrics: how you measured agent reliability and what you changed after failure-mode analysis.

* Practice narrating your code and evaluation reasoning aloud with Nora AI's Technical Mode so tradeoffs on accuracy, cost, and latency come out clearly.

Round 4: Behavioral, Ownership, and Stakeholder Partnership (~45 to 60 minutes)

What to Expect

Likely with the Head of New Product Engineering or a senior leader, this round tests the ownership and ambiguity mindset the posting demands. Expect STAR-style stories about scoping undefined work, partnering with non-technical business teams (Revenue Ops, Marketing, Finance Ops, Talent), prioritizing high-impact automation, and driving measurable business results. They want to see that you define what to build, move fast, act like an owner, and hold a high bar.

Example Questions

* "Tell me about a time you partnered with a non-technical team to turn their real workflow into a technical solution."

* "Describe how you prioritized which workflows to automate when everything felt urgent."

* "Walk me through a project where you owned the outcome end to end and measured the results."

* "Tell me about a time an AI system failed in production. How did you respond and what did you change?"

Tips

* Prepare four to six STAR stories that highlight ownership, ambiguity, cross-functional partnership, and quantified business impact.

* Show how you translate messy stakeholder workflows into ranked, high-impact automation decisions.

* Rehearse these stories with Nora AI's Behavioral Mode so your ownership and stakeholder examples stay structured and outcome-focused under follow-up questions.

Frequently Asked Questions (FAQ)

1) How many rounds are there?

Expect roughly 4 to 5 rounds: a recruiter screen, one or two technical rounds (agentic system design plus Python/backend/evaluation), and a behavioral/ownership round with the hiring leader. Socure does not publish its exact loop for this role, so treat this as the typical structure for a Staff-level AI engineering role at a company of this type.

2) What topics are most common?

* Agentic architectures (planner-executor, reflection, stateful agents, human-in-the-loop) and frameworks like LangGraph, CrewAI, or AutoGen

* Python backend/APIs, evaluation and observability, RAG/memory, and operating in ambiguity with real business impact

3) How long does the process take?

Generally about 3 to 5 weeks from recruiter screen to final decision, depending on scheduling. Staff-level loops with multiple technical and leadership conversations can run on the longer end.

4) How should I prepare?

* Review the posting's stack: LLM APIs, at least one agentic framework, Python backend/data pipelines, evaluation frameworks, and AWS LLM infrastructure.

* Prepare concrete stories of shipping AI automation that changed business operations, with metrics for reliability and impact.

* Refresh agent safety and reliability practices: prompt injection prevention, hallucination mitigation, guardrails, and failure-mode analysis.

* Practice with Nora AI: use Standard Mode for the recruiter screen, Technical Mode for the agent design and Python/evaluation rounds, Behavioral Mode for ownership and stakeholder stories, and Salary Negotiation Mode to handle the $220K to $290K plus equity and bonus offer conversation.