AI Platform Engineering Team Lead (Sovereign AI)

TL;DR

AI Platform Engineering Team Lead (Sovereign AI): Architect and evolve agentic AI platform including orchestration frameworks, LLM gateways, evaluation infrastructure, tool-calling systems, and retrieval pipelines with an accent on reliability, scalability, and deployment across cloud and on-prem environments. Focus on leading small team of AI engineers, setting technical direction through RFCs and design reviews, and ensuring production readiness for sovereign AI cybersecurity products.

Location: Tel Aviv

Company

hirify.global builds AI cybersecurity platform combining AI and human expertise to protect nations and critical infrastructure with sovereign AI operating in on-premise, private cloud, and air-gapped environments.

What you will do

• Architect and evolve AI platform components like agent orchestration, LLM gateways, context engineering pipelines, evaluation infrastructure, tool-calling, and retrieval pipelines via RFCs, prototypes, and design reviews.
• Lead and grow small team of AI Engineers: hire, mentor, pair on problems, conduct code and design reviews.
• Contribute to critical systems, debug production incidents, and maintain codebase context for technical decisions.
• Own reliability of AI and agent services: set SLAs, build observability for non-deterministic systems, harden tool environments for cost and security.
• Set standards for AI engineering practices including agent testing, evaluation frameworks, retrieval benchmarks, and CI/CD for AI systems.
• Collaborate with ML Platform, Data Platform, DevOps, Data Science, and Product teams to evolve platform for agentic workflows organization-wide.
• Measure and improve developer experience metrics like deploy friction, onboarding time, and CI turnaround.

Requirements

• 6+ years in backend software engineering, 4+ years on production systems integrating AI/ML models or LLMs.
• 2+ years leading engineering team: hiring, mentoring, design reviews, shipping alongside team.
• Strong Python, Go, or Java; system architecture, API design, testing, secure coding.
• Deep knowledge of agentic systems, LLM integration, agent orchestration, tool-use architectures, context engineering, frameworks like LangGraph.
• Experience building/operating production APIs, services, platform infrastructure at scale; relational DBs, message queues, event-driven architectures.
• Production RAG pipelines, vector databases, embedding systems, retrieval quality.
• LLM/agent eval infrastructure, monitoring AI quality, observability for non-deterministic systems.

Nice to have

• Platform/infra: Kubernetes, AWS, Terraform/IaC, CI/CD, service architecture, incident management.
• Experience with MCP or similar tool-use protocols for agent-to-service communication.
• Hands-on ML: model training, fine-tuning, ML pipelines.

Culture & Benefits

• AI-first culture where every team builds and operates agents.
• Passion-driven team focused on real-world challenges in AI and security.
• Opportunity to make meaningful impact on sovereign AI products protecting critical infrastructure.