{"id":2501816,"url":"https://alion.io/job/biocatch-ai-software-infra-engineer","title":"AI & Software Infra Engineer","company":{"id":178269,"name":"BioCatch","domain":"biocatch.com","url":"https://alion.io/company/biocatch","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Comeet","truth_index":null},"role":"DevOps","role_family":"DevOps","seniority":"senior","employment_type":"full_time","work_mode":"hybrid","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":{"min_usd":70000,"max_usd":194000,"period":"year","method":"global_role_cell_scaled_by_country","sample_n":3074},"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agentic Workflows","optional":false},{"name":"AI Agents","optional":false},{"name":"Anthropic","optional":false},{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"CI/CD","optional":false},{"name":"Claude","optional":false},{"name":"Context Engineering","optional":false},{"name":"Docker","optional":false},{"name":"FastAPI","optional":false},{"name":"Function Calling","optional":false},{"name":"Git","optional":false},{"name":"GitHub Actions","optional":false},{"name":"GitOps","optional":false},{"name":"Helm","optional":false},{"name":"Human-in-the-Loop","optional":false},{"name":"Jenkins","optional":false},{"name":"Kubernetes","optional":false},{"name":"LangChain","optional":false},{"name":"LangGraph","optional":false},{"name":"LLM","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"Machine Learning","optional":false},{"name":"Model Context Protocol","optional":false},{"name":"Multi-Agent Systems","optional":false},{"name":"OpenAI","optional":false},{"name":"OpenAI Agents SDK","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"Structured Outputs","optional":false},{"name":"TypeScript","optional":false}],"status":"live","first_seen_at":"2026-10-13T13:50:32Z","employer_posted_date":"2026-10-13","last_verified_at":"2026-10-16T13:15:01Z","board_verified":true,"closed_at":null,"days_open":2,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":2},"description":"Description\nBioCatch is the leader in Behavioral Biometrics - a technology that leverages machine learning to analyze an online user's physical and cognitive digital behavior to protect individuals online. Our mission is to unlock the power of behavior and deliver actionable insights to create a digital world where identity, trust, and ease coexist.\nToday, 34 of the world's largest 100 banks and 210 total financial institutions rely on BioCatch Connect™ to combat fraud, facilitate digital transformation, and grow customer relationships. BioCatch's Client Innovation Board - an industry-led initiative including American Express, Barclays, Citi Ventures, and National Australia Bank - helps us identify creative and cutting-edge ways to leverage the unique attributes of behavior for fraud prevention. With over a decade of analyzing data, more than 80 registered patents, and unparalleled experience, BioCatch continues to innovate to solve tomorrow's problems.\nFor more information, visit www.biocatch.com.\nWe are looking for a proactive and skilled AI & Software Infra Engineer with a strong Agentic AI focus to join our team. In this role, you will design and build the infrastructure, tooling, and AI-powered automation that keep our engineering systems reliable, scalable, and intelligent.\nYou will be responsible for embedding GenAI and agentic capabilities into our internal developer platforms and CI/CD workflows - creating frameworks, libraries, and multi-agent systems that accelerate automation, improve system stability, and unlock new levels of engineering productivity. This is a hands-on role for someone who thrives at the intersection of infrastructure engineering and applied AI.\nThe ideal candidate brings deep technical expertise in Python (FastAPI) and/or TypeScript, Kubernetes, CI/CD (Jenkins, GitHub Actions), containerization, and cloud infrastructure - combined with hands-on experience building LLM-powered applications, agents, MCP servers, and RAG pipelines.\nWhat you'll be doing:\nAI Platform Development: Design and build internal AI platforms, reusable SDKs, libraries, tools, and MCP servers for agentic applications.\nAgentic Workflow Development: Develop single- and multi-agent workflows covering planning, tool selection, delegation, state and memory management, execution, retries, fallbacks, error recovery, and human-in-the-loop approval.\nAI-Powered Infrastructure & CI/CD: Integrate AI capabilities into Kubernetes infrastructure and Jenkins/GitHub Actions pipelines to improve development, testing, deployment, and operational workflows.\nAI Observability & Evaluation: Implement agent tracing, prompt and tool-call logging, latency and error monitoring, token and cost tracking, regression testing, and failure analysis.\nAI Security & Governance: Apply guardrails, access controls, auditability, and governance practices to ensure agents operate safely and reliably.\nEnd-to-End Platform Ownership: Own AI platform capabilities from architecture through production and build reusable solutions that accelerate adoption across engineering teams.\nCross-Functional Collaboration: Collaborate with engineering and business teams to identify high-impact AI opportunities and translate them into scalable technical solutions.\nAI Research & Innovation: Evaluate emerging AI frameworks, models, and cloud technologies and promote their adoption where they provide measurable value.\nRequirements\nAI & GenAI\nAI Experience- Mandatory: Minimum of 2 years of hands-on production experience building LLM-powered applications, AI agents, multi-agent systems, or internal AI platforms.\nHands-on experience with LangChain, LangGraph, Claude Agent SDK, and OpenAI and/or Anthropic APIs.\nStrong understanding of agent architecture, including planning, tool selection, delegation, state and memory management, retries, fallbacks, error recovery, and human-in-the-loop workflows.\nExperience with prompt and context engineering, tool/function calling, and structured outputs.\nExperience building and integrating MCP servers and clients, including tool schemas, permissions, reliable execution, and error handling.\nExperience implementing AI evaluations and regression tests that measure agent quality, task completion, tool usage, behavioral changes, and failure modes.\nExperience with AI observability, including traces, prompts, tool calls, latency, errors, token consumption, costs, and production debugging.\nUnderstanding of AI guardrails, security controls, auditability, governance, and safe agent execution.\nAbility to build reusable AI platform capabilities that accelerate adoption across engineering teams.\nCritical thinking and apply sound judgment to AI inputs\nCore Engineering\nMinimum of 5 years of hands-on development experience with Python and/or TypeScript.\nExperience building asynchronous backend services and APIs using FastAPI or an equivalent modern framework.\nStrong software engineering, analytical, troubleshooting, and cross-functional collaboration skills.\nAbility to build reliable, reusable internal platforms that improve developer productivity and accelerate technology adoption.\nInfrastructure & DevOps\nStrong experience with Kubernetes and Helm, including deployment, networking, resource management, autoscaling, troubleshooting, chart development, templating, versioning, and release management.\nStrong experience with Docker, Jenkins, GitHub Actions, and AWS and/or Azure.\nFamiliarity with Infrastructure as Code, GitOps, and Git-based collaborative 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