{"id":3608319,"url":"https://alion.io/job/zafran-security-senior-ai-engineer","title":"Senior AI Engineer","company":{"id":177893,"name":"Zafran Security","domain":"zafran.io","url":"https://alion.io/company/zafran-security","size_band":"51-200","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Ashby","truth_index":{"grade":"B","score":79,"open_postings":6,"ghost_share":0,"stale_share":0.833,"repost_share":0,"time_to_fill_p50_days":48,"computed_at":"2026-11-08T05:47:12Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Tel Aviv, Israel"],"countries":["IL"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"AWS","optional":false},{"name":"Function Calling","optional":false},{"name":"Kubernetes","optional":false},{"name":"LLM","optional":false},{"name":"Model Context Protocol","optional":false},{"name":"Python","optional":false},{"name":"Structured Outputs","optional":false},{"name":"AWS Bedrock","optional":true},{"name":"AWS Strands Agents","optional":true},{"name":"ClickHouse","optional":true},{"name":"ElasticSearch","optional":true},{"name":"Go","optional":true},{"name":"LiteLLM","optional":true},{"name":"LLM Guardrails","optional":true},{"name":"OpenTelemetry","optional":true},{"name":"PostgreSQL","optional":true}],"status":"live","first_seen_at":"2026-11-02T09:17:05Z","employer_posted_date":"2026-11-02","last_verified_at":"2026-11-08T06:20:27Z","board_verified":true,"closed_at":null,"days_open":6,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":6},"description":"Zafran is building the next generation of vulnerability and risk management. We bring together complex security data and turn it into clear investigation, prioritization, and action.\nWe're creating a new AI team to build the shared infrastructure, tools, and standards that enable teams across R&D to develop reliable AI-powered capabilities. The team will also own and deliver core AI experiences used across Zafran's platform and organization.\nAbout Zafran:\nOur Mission: To stop the exploitation of vulnerabilities, everywhere.\nWhat makes us different: Zafran de-risks 90% of critical vulnerabilities overnight across your hybrid environment, leveraging Agentic Capabilities and your existing security tools to rapidly mitigate and remediate the 10% most likely to be exploited.\nWho’s behind us: Zafran is backed by Menlo Ventures, Sequoia Capital, Cyberstarts, and a deep belief that cybersecurity should move as fast as attackers do. We’re one of the fastest-growing companies in the industry, scaling to meet demand from the world’s most advanced, security-obsessed organizations.\nWe’re serious about our mission- so expect work that matters, teammates who challenge and inspire you, and plenty of fun along the way!\nWe're looking for a Senior AI Engineer to be one of the team's first engineers and to own Zafran's AI infrastructure end to end - the shared platform every R&D team uses to build, test, ship, and monitor AI features. That platform powers our in-product AI assistant, our background agents, and every AI capability still to come. You'll set the standard for how AI runs in production at Zafran: when a team ships an AI feature, it should be reliable, secure, observable, and cost-efficient by default - because your platform made it that way.\nWhat you will do:\nOwn the AI platform. Design, build, and run the shared agent runtime: model access, tool use, memory and sessions, streaming, and human approval for sensitive actions.\n\nProduction-grade. Scaling, fallback between model providers, rate limits, timeouts, and safe rollouts on Kubernetes.\n\nOwn evaluation. Build the evaluation framework every team uses to measure quality, correctness, latency, and cost - and catch regressions in CI before customers do.\n\nOwn observability and cost. Tracing for every agent run and tool call, token spend per tenant and per feature, and alerts when quality or cost drifts.\n\nOwn AI security. Strict tenant isolation, defenses against prompt injection, and guarded data access across a multi-tenant security platform.\n\nBuild the tool layer. MCP tools and data connectors that let agents query our security data safely and reliably.\n\nEnable R&D. Shared libraries, templates, and docs so other teams can build, test, deploy, and monitor AI features independently.\n\nStay hands-on with architecture, implementation, and production issues, and help raise the AI engineering bar across R&D.\n\nRequirements:\n5+ years of backend or infrastructure engineering, with strong system design skills.\n\nProven experience building and operating production-grade AI / LLM infrastructure - real users, real traffic, real incidents. Not prototypes.\n\nDeep hands-on experience with agentic systems: tool use, structured outputs, prompt and context management, retrieval, and long-running or streaming flows.\n\nExperience designing and running LLM evaluations at scale.\n\nStrong Python skills.\n\nProduction experience on AWS and Kubernetes, including observability (tracing, metrics, logs).\n\nOwnership mindset - you treat the platform as a product and other engineers as your customers.\n\nExperience with the following is a plus:\nExperience with Amazon Bedrock, Strands Agents, LiteLLM, MCP, or OpenTelemetry (our current stack).\n\nExperience building internal developer infrastructure used by multiple engineering teams.\n\nExperience with model evaluation, safety controls, governance, or LLM security (guardrails, prompt injection).\n\nExperience operating multi-tenant SaaS systems.\n\nBackground in cybersecurity or security-data products.\n\nFamiliarity with Go, ElasticSearch, ClickHouse, or Postgres.\n\nWhy this role matters:\nAI is a shared capability across Zafran's products and engineering organization. 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