Requirements:
Basic qualification:
Minimum of 2 years of experience managing and leading a software engineering team, specifically in an environment with cross-functional roles (e.g., managing both backend and frontend specialists).
Minimum of 5 years of hands-on experience in software development, engineering, or a related technical role.
Strong experience in backend development using Python/Node.JS
Demonstrated experience working with messaging queues for high-throughput, asynchronous processes. Experience with RabbitMQ is a significant advantage.
Proven ability to drive development across the full stack, including professional experience with a modern front-end framework (e.g., React, Angular, or Vue).
Expert-level knowledge and practical experience designing and consuming various API architectures, specifically GraphQL and REST.
Bachelor's degree in Computer Science, Engineering, or a related technical field.
Professional fluency in English (written and verbal) is required.
Other qualification:
Experience with Python Celery or similar frameworks for managing asynchronous and long-running background tasks.
Familiarity with Kubernetes (k8s) environments and GitOps tools like ArgoCD for continuous delivery and deployment.
Experience designing and implementing complex, multi-step data or processing pipelines using tools like Argo Workflows, Airflow, Prefect.
Direct experience building or maintaining solutions that involve MS Teams/Slack bots/agents and/or RPA/workflow automation systems.
Previous experience in the HR Technology (HR Tech), Talent Acquisition, or Enterprise SaaS domain.
Proven experience in defining quality strategies, working with QA teams, and driving CI/CD pipelines.

Requirements:
You've shipped agentic systems to production - real orchestration, tool use, structured outputs, and the failure modes that only appear at scale. Not "I've called an LLM API."
You've built the evaluation discipline, not just consumed it: trajectory tests, golden datasets, regression gates, offline replay. "It seems better" is not a metric, and you have opinions about what is.
Deep backend and distributed-systems engineering. Strong Python, and comfort with workflow orchestration (Temporal or equivalent), streaming, and cloud-native infrastructure. Agent platforms are systems problems wearing an AI hat.
Fluency across the modern agent stack - LangChain/LangGraph-style frameworks, multi-provider routing, structured output contracts, prompt and context engineering - with the judgment to know which parts are load-bearing and which are fashion.
Security literacy. Enough to reason about whether an agent's security output can be trusted, and to argue with researchers on the merits. You don't need to be a vulnerability researcher.
Influence without authority. You'll change how three teams work with no one reporting to you. Show us where you've done that.
Advantage: experience with AI/LLM security - red-teaming agents, prompt injection, or agentic attack patterns.
Advantage: background in cybersecurity, detection engineering, or WAF/mitigation systems.
Advantage: you've driven AI adoption across a whole company, not only an engineering org.