AI Engineering Manager role at Plenti leading the design, deployment and scale of AI-driven solutions for operational and sales efficiency.
Responsibilities
Lead, mentor and develop a small team of engineers, supporting career growth and technical excellence
Design and manage AI-specific infrastructure, taking ownership of AI platforms, specialized Kubernetes nodes, and the underlying environments required for model orchestration
Deliver end-to-end AI solutions designed to improve operational and sales efficiency, ensuring they are integrated into multiple core platforms
Build sophisticated workflows and autonomous components using Python, Langflow and emerging AI tooling
Drive the full solution lifecycle including discovery, requirements documentation, architecture, build, testing and deployment
Collaborate with SRE and Platform teams to align AI infrastructure with organisational patterns while enabling flexibility needed for AI innovation
Architect scalable and resilient AI-driven components embedded into existing applications to enhance user experience and business logic
Manage and prioritise the backlog across foundational AI platform work and rapid feature delivery
Establish best practices for testing, documentation and monitoring of AI solutions to ensure reliability and performance in production environments
Stay current with AI trends and evaluate new tools to continuously uplift our internal AI capability.
Requirements
7+ years in technology roles, with a strong software engineering foundation
2+ years of experience in a formal leadership or engineering mentorship role, with a track record of mentoring technical talent
Deep knowledge of the modern AI stack, including agentic frameworks and AI techniques such as RAG
Advanced proficiency in Python for application development and data processing
Proficiency in AWS services and Infrastructure-as-Code (Terraform) to manage specialized AI compute resources
Familiarity with Docker and Kubernetes, specifically regarding how to deploy and scale AI-specific workloads
Ability to perform requirements discovery and translate business problems into technical architectures
Machine learning knowledge is desirable but the focus of this role is on engineering implementation and integration rather than model research.
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