tenex - Senior AI/ML Engineer
Requirements
• SOFTWARE ENGINEERING & ARCHITECTURE EXPERTISE • Core Engineering: 7+ years of experience in software development, engineering production systems using modern programming languages (Python, Go, Rust, or Java). • Agentic Systems: Deep knowledge of agentic systems design, such as Centralized and/or Decentralized MAS (Multi-Agent Systems) architectures. • Graph Architectures: Solid understanding of Graph structures and specifically graph databases. • Orchestration Frameworks: Hands-on experience building agents, orchestration frameworks (LangChain/LangGraph, Agno AGI, or custom), and evaluation harnesses. • Distributed Systems: Deep understanding of microservices architecture, containerization (Docker, Kubernetes), and event-driven systems. • APIs: Strong fundamentals in API design (REST/gRPC) and distributed systems. • Communication: Clear, concise communication skills and a bias for collaborative problem-solving. • Leadership Alignment: Proven track record of gathering consensus and guiding multi-stakeholder initiatives through uncertain boundaries. • Analytical Rigor: Strong problem-solving and analytical skills. • Domain Background: Prior work in cybersecurity (SIEM, EDR, SOAR, or MDR). • Startup Mentality: Background driving high-impact engineering initiatives in high-growth startups or enterprise SaaS. • Cloud Infrastructure: Familiarity with cloud infrastructure security (AWS, GCP, or Azure). • EDUCATION & CERTIFICATIONS • Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field. • Relevant certifications (AWS/GCP Professional Engineer, Kubernetes, or security-related credentials) are a plus.
Responsibilities
• Project Execution: Lead and own the architecture and delivery of technical components of complex projects. This means communicating effectively to align on requirements, executing on high-quality code, and collaborating with senior engineers and stakeholders throughout the development lifecycle. • AI Layer Engineering: Design & build the AI layer that powers autonomous detection, RAG-backed investigation, and auto-remediation workflows. • Productionize Reasoning Engines: Develop and productionize large-scale LLMs, graph-based reasoning engines, and streaming feature pipelines that operate on billions of security events. • Evaluation & Reliability: Own evaluation & reliability—from prompt libraries and fine-tuning to red-team testing, latency budgets, and fallback strategies. • Cross-Functional Collaboration: Lead cross-functional initiatives, partnering with Product, Detection Engineering, and Customer Success to translate real-world attacker behavior into robust ML and rule-based detections. • Push the Frontier: Experiment with retrieval-augmented generation, tool-calling agents, and multi-modal models (text + logs + graphs) to keep defenders decisively ahead. • Mentorship: Provide technical mentorship to junior engineers, foster engineering best practices, and contribute to architectural design reviews.
Benefits
• Opportunity to work with cutting-edge AI-driven cybersecurity technologies and Google SecOps solutions. • Collaborate with a talented and innovative team focused on continuously improving security operations. • Competitive salary and benefits package. • A culture of growth and development, with opportunities to expand your knowledge in AI, cybersecurity, and emerging technologies. • If you're passionate about combining cybersecurity expertise with artificial intelligence and have experience with advanced multi-agent architectures, we encourage you to apply!
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