Head of Engineering - Agentic AI Platform

About the role

AUMET builds next-generation agentic AI systems and is looking for a hands-on Head of Engineering to architect, scale and secure the technology platform behind its AI-driven solutions. The role suits an experienced engineering leader combining deep technical expertise in distributed systems, cloud infrastructure, backend engineering and DevOps with a strong understanding of enterprise-scale AI systems.

Key responsibilities

• Design and scale distributed, fault-tolerant systems for complex, asynchronous, multi-agent AI workflows, and architect high-performance APIs, microservices, API gateways and integrations.
• Optimize relational, NoSQL and vector databases; build secure, multi-tenant SaaS architectures with data and workload isolation.
• Own cloud architecture (AWS, GCP or Azure), containerization with Docker and Kubernetes, and Infrastructure as Code and CI/CD.
• Implement monitoring, alerting and distributed tracing with tools such as Grafana, New Relic, Prometheus and OpenTelemetry.
• Establish AI governance policies, model registries, risk classification and automated approval workflows, and support compliance with SOC 2, ISO 42001, NIST AI RMF and GDPR.
• Lead, mentor and scale backend, infrastructure, DevOps and AI engineering teams, and set engineering standards and architectural best practices.

Required profile

• Proven experience designing and scaling distributed, cloud-native systems.
• Strong backend architecture, microservices and API design expertise, and hands-on SQL, NoSQL and vector database experience.
• Hands-on experience with observability tools, especially Grafana and New Relic.
• Demonstrated engineering leadership, including mentoring teams and driving technical strategy.

Required skills

• Cloud platforms: AWS, GCP or Azure.
• Docker, Kubernetes and Infrastructure as Code, particularly Terraform.
• High-concurrency systems, asynchronous programming and distributed messaging (e.g. Kafka, RabbitMQ, Redis, AWS SQS, Celery, Temporal).
• Practical understanding of LLM APIs, prompt engineering and agentic workflows.
• Preferred: workflow orchestration tools (Temporal, Airflow, Prefect, LangGraph) and AI observability platforms (LiteLLM, LangSmith, Arize, Helicone).