AI Engineer

Description What we’re looking for: We're looking for an AI Engineer to take a central role in shaping and evolving the intelligence layer of Linx, an AI-native identity platform. You’ll help build the AI brain behind our assistant, Autopilot, and the identity-specific agents that monitor, investigate, govern, and remediate identity risk for some of the world’s largest enterprises. This is a hands-on role for someone who drives technical direction, raises engineering standards, and brings clarity to complex systems. At Linx, complexity is the baseline: you’ll work on a graph-based Identity Fabric that unifies human, non-human, and agentic identities across enterprise environments, where a wrong model output isn’t a demo bug, but a security risk. You’ll work end-to-end, from research and design through production, partnering with backend, security research, product, and data teams to build scalable, reliable, and trustworthy AI systems. What You'll Do Lead the design and implementation of AI-powered systems end-to-end, across our AI assistant, Autopilot (autonomous agents for real-time identity risk detection and remediation), and future identity-specific agents. Integrate LLMs into security-critical workflows, beyond prompting into tool use, structured outputs, function calling, retrieval strategies, model routing, and orchestration. Own the full lifecycle, from problem definition and data design to prompting, evaluation, and production, while defining quality and building the systems to measure it. Engineer for trust and control - guardrails, validation layers, and human-in-the-loop mechanisms that enable safe autonomy. Evolve our AI-native data layer (Identity Graph, DBs, analytics, APIs), recognizing that AI quality is fundamentally a data problem. Drive architectural decisions balancing scalability, latency, and cost across large, multi-tenant systems. Collaborate closely with backend, product, security research, and data teams across identity, automation, and governance domains. Establish best practices for building and deploying LLM systems: testing, observability, and reliable agent behavior at scale. Continuously experiment and refine: ship fast, measure with real signals, and improve across prompts, tools, retrieval, and data. Requirements What You'll Bring Extensive experience in backend, AI, or data-intensive systems, with a track record of building production systems used by real customers. Hands-on experience with LLMs in production, including prompting, tool use, function calling, orchestration, and evaluation, with a strong understanding of real-world failure modes. Strong ability to design and build end-to-end systems, from data pipelines and retrieval to deployment, monitoring, and iteration. Deep understanding of data design for AI systems, including schemas, semantics, and workflow structuring for reliable outputs. Strong coding skills in Python (or similar) and experience working in modern backend systems at scale, with awareness of latency, cost, and multi-tenancy. Proven ability to drive architectural decisions and technical direction in ambiguous, high-impact areas. Pragmatic, product-oriented mindset, focused on solving real customer problems with the right tools. Strong communication skills, with the ability to explain trade-offs and align across teams. Passion for AI and a builder mindset, with a focus on ownership, speed, and impact. Nice to Have: Experience building Text-to-SQL, Text-to-Query, or NL-to-data systems over complex or graph-based data. Experience building agentic systems in production, including tool routing, long-running workflows, and failure handling. Hands-on experience with RAG, hybrid retrieval, semantic search, and learning from user interactions. Exposure to MCP, agent-facing APIs, or data access layer design for AI systems. Background in identity or security domains (IAM, IGA, PAM, etc.). Experience designing LLM evaluation frameworks, including benchmarks, metrics, and human-in-the-loop review.