Senior AI Engineer

Socure Socure · Vertical AI · United States · Remote · Tech

Senior AI Engineer to design and ship agentic systems for internal workflows, leveraging an existing Agentic AI Foundations platform. This role involves partnering with business teams, developing multi-step, tool-augmented agents, integrating with platform primitives, and contributing to reusable design patterns. The goal is to drive efficiency and intelligence across the enterprise, transforming Socure into an AI-native company.

What you'd actually do

  1. Partner with business teams (e.g., Revenue Ops, Marketing, Finance Ops, Talent Acquisition) to catalog manual, high-frequency workflows and rank them by impact, feasibility, and urgency.
  2. Build multi-step, tool-augmented agent workflows that can plan, execute, observe outcomes, and iteratively improve. Design and implement planner–executor and reflection-based architectures to enhance reasoning quality and task reliability. Develop stateful agent systems and incorporate human-in-the-loop controls, including approval gates, fallback paths, and escalation mechanisms.
  3. Leverage the Agentic AI Foundations team's platform covering agent runtime, orchestration, memory, tool registry, guardrails, and observability. Provide feedback that shapes platform priorities.
  4. Leverage LLMs, multi-agent frameworks, and orchestration platforms to create differentiated internal solutions that can't be bought off the shelf. Stay ahead of emerging AI technologies and regulatory frameworks to ensure Socure leads the industry in secure, compliant, and intelligent internal systems.
  5. Discover reusable agent design patterns while building real workflows and contribute them back to the Agentic AI Foundations paved paths so the work compounds across Socure.

Skills

Required

  • 8+ years of software engineering experience, with at least 2 years focused on AI/ML systems or LLM-powered applications in production.
  • Deep hands-on experience with LLM APIs and at least one agentic framework (e.g. LangGraph, CrewAI or AutoGen).
  • Strong Python skills and experience building production-grade backend services, APIs, and data pipelines.
  • Proven ability to operate in ambiguity: you've walked into an undefined problem space, figured out what to build, built it, and measured the results.
  • Experience shipping AI/automation solutions that directly impacted business operations.
  • Strong systems thinking: you design for reliability, observability, and maintainability from day one, not as an afterthought.
  • Ability to collaborate directly with non-technical business stakeholders to understand their actual workflows and translate those into technical solutions.

Nice to have

  • Experience with multi-agent systems, workflow orchestration, or complex tool-use patterns in production.
  • Familiarity with evaluation frameworks (e.g., LangSmith, Weights & Biases, Arize, or custom eval pipelines) for AI systems (agent reliability, accuracy benchmarking, failure-mode analysis).
  • Experience with RAG pipelines, vector databases, knowledge graphs, or memory/grounding systems.
  • Experience with real-time or streaming AI systems.
  • Familiarity with AI safety and security practices, including prompt injection prevention, hallucinations mitigation and data protection/privacy.
  • Background in a regulated industry (fintech, healthcare, government) where compliance and security review processes are standard.
  • Experience with agent skill abstraction and structured tool integration via MCP (Model Context Protocol), function calling or similar protocols.
  • Experience with AWS-hosted LLM infrastructure (Bedrock, AgentCore/Strands, Lambda, SageMaker) or equivalent cloud ML services.

What the JD emphasized

  • define _what_ to build - not just execute what's been scoped
  • first major internal partner building on Socure's Agentic AI Foundations platform
  • ship faster
  • real-world pull-through
  • AI-native company
  • operate in ambiguity
  • figured out what to build, built it, and measured the results
  • shipping AI/automation solutions that directly impacted business operations
  • design for reliability, observability, and maintainability from day one
  • collaborate directly with non-technical business stakeholders

Other signals

  • building agentic systems
  • internal productivity tools
  • AI-native company
  • intelligent automation