Build the Future Workforce
Wand turns AI into labor. It enables humans and AI agents to operate together as a unified, hybrid workforce, with comprehensive management and oversight. And it’s already operating at scale inside some of the world’s largest organizations.
Wand built the world’s first Agentic Labor Infrastructure enabling governments and global enterprises to create, manage, and scale digital workforces.
Our mission is to integrate agent ecosystems into the core of work and business, unlocking a generational leap in the global economy. We’re building the infrastructure that lets humans and AI agents operate together safely, transparently, and at scale.
Join Wand in leading the Agentic Shift
Wand is building a high-performing global team who take full ownership of what they build. We lead by example, move fast, make data-aware decisions, and continuously push for more- always with a focus on delivering real value to customers.
You would be joining a world-class team that combines deep research expertise and real-world product execution, with experience spanning Deepmind, Google, Amazon, Miro, Elise AI, IBM and Accern.
Position Summary
We are hiring for a highly experienced Senior Staff SRE Engineer to act as a senior technical authority within our reliability function.
This is a deeply hands-on individual contributor role, to build and operate SRE practices at scale. You will design and evolve resilient infrastructure, drive reliability across multiple engineering streams, and ensure our AI-driven products operate with high availability, performance, and security.
You will work across platform, product, data, and ML teams, helping us productionise models, absorb and standardise customer environments, strengthen Kubernetes-based architecture, and mature our CI/CD pipelines end-to-end.
You will also collaborate with other Staff engineers and Architects to shape the global product architect and technology vision.
Responsibilities
Architect, deploy, and operate scalable, secure production environments (AWS preferred).
Lead reliability improvements across multiple engineering streams.
Design and evolve Kubernetes-based infrastructure, including migration and optimisation initiatives.
Build and enforce strong Infrastructure-as-Code standards.
Define and operationalise SLIs, SLOs, and error budgets.
Strengthen observability across applications, infrastructure, data pipelines, and ML systems.
Work closely with product and data teams to integrate model analytics and product telemetry into reliability insights.
Work across and optimise the entire CI/CD pipeline, from build to deploy to rollback.
Improve release safety, deployment frequency, and predictability of SLAs.
Lead incident response for complex cross-system failures and drive postmortems.
Reduce operational toil through automation and platform engineering improvements.
Design processes and tooling to absorb, standardise, and troubleshoot customer environments.
Support and productionise ML workloads (MLOps practices including model deployment, monitoring, retraining workflows).
Ensure infrastructure aligns with enterprise-grade security and regulatory requirements.
Mentor engineers and raise the overall reliability bar across teams.
Key Requirements
Extensive hands-on experience in SRE or Production Engineering roles.
Demonstrated experience building or scaling SRE practices in high-growth or complex environments.
Deep expertise in AWS or Azure-based cloud infrastructure.
Strong experience with Kubernetes (including migration, scaling, and production hardening).
Advanced Infrastructure-as-Code experience (Terraform or equivalent).
End-to-end CI/CD pipeline design and optimisation experience.
Strong experience with observability tooling across distributed systems.
Experience troubleshooting complex multi-tenant or customer-hosted environments.
Experience supporting production data platforms and ML systems.
MLOps experience, including model deployment and monitoring.
Strong understanding of distributed systems, scalability, and fault tolerance.
Systems thinker who understands interactions across infrastructure, product, data, and ML.
Excellent communication skills and ability to work cross-functionally.
Preferred Experience
Experience in large-scale global B2B/B2C products.
Experience working with AI/ML systems, NLP, or LLM-based products.
Experience integrating product analytics and model performance metrics into operational monitoring.
Background in enterprise environments with strong security and compliance requirements.
Experience implementing regulatory controls within cloud infrastructure.
Experience scaling infrastructure during rapid growth phases.
Experience evaluating infrastructure tooling and vendors.
Experience in collaborating with large scale enterprise customers to deploy and operate environments within their accounts and VPCs.
Personal Characteristics
Strong problem solver who anticipates failure modes.
High ownership mentality and accountability.
Comfortable working across streams and influencing without formal authority.
Learning-oriented with a drive for continuous improvement.
