AI Technical Lead

Location: ~HOUSTON, TX
Duration: 6 months





Focus Areas:

· Lead the pod's day-to-day development and technical decisions.

· Translate product objectives into implementation plans and sprint goals.

· Directly manage developers and AI engineers.


Leadership & Collaboration Expectations

· Ability and willingness to collaborate with Perth based Engagement Lead on a daily basis supporting the discovery phase of initiatives

· Lead technical design decisions and resolve blockers across pods.

· Manage code reviews, shared modules, and ensure engineering consistency.

· Drive agile development practices including sprint planning, backlog grooming, and velocity tracking.

· Foster a culture of growth mindset, technical excellence, encouraging experimentation, learning from failure, and continuous improvement.

· Support onboarding and mentoring of developers and AI engineers.

· Engage with business stakeholders when needed (Inform Engagement Lead).


Communication & Stakeholder Engagement Expectations

· Work closely with the Tech Chapter Lead and Engagement Lead to align technical decisions with the AI platform strategy, business goals, and cross-team dependencies.

· Represent the pod in technical forums, showcases, and architecture reviews.

· Communicate risks, trade-offs, and technical constraints clearly to non-technical stakeholders.

· Ensure timely updates to the Engagement Lead on technical decisions and stakeholder conversations.


Technical Skills


Engineering & Architecture

· Strong experience with cloud-native development, including secure handling of secrets, environment variables, and service configurations.

· Proficiency in CI/CD pipelines for AI systems and microservices.

· Familiarity with team-based access control, code ownership, and repository governance.

· Skilled in modern programming languages and infrastructure-as-code practices.

· Ability to manage API specifications and service integrations across platforms.

· Experience agile software development

AI & Agentic Systems

· Experience designing and deploying conversational agents and multi-agent systems.

· Capable of orchestrating agent workflows, managing context, and integrating external data sources.

· Understanding of retrieval-augmented generation (RAG) patterns and scalable inference strategies.

· Comfortable evaluating trade-offs between deployment models (e.g. serverless vs. containerised).

Security & Compliance

· Awareness of enterprise data privacy and cybersecurity standards.

· Responsible handling of user interactions and logs, with clear boundaries for data retention and observability.

Tooling & Observability

· Use of observability tools for tracing, logging, and performance monitoring.

· Familiarity with API testing and authentication flows across service layers.