Careers
Build AI Infrastructure That Actually Runs.
Novera Intelligence hires engineers who want their work to run in production — measured, operated and dependable. No theater, no shelfware.
- Direct access to client leadership
- Written engineering culture
- Global delivery, deep specialization
Disciplines
Where Our Engineers Work
Six disciplines across the AI delivery lifecycle. Deep specialization within a discipline, genuine breadth across the stack.
AI Infrastructure Engineering
Design and operate the accelerated computing layer: GPU clusters, high-performance networking, storage and scheduling.
- Cluster architecture
- InfiniBand & Ethernet fabrics
- Storage architecture
- Slurm & Kubernetes scheduling
- Facilities coordination
ML & Workload Engineering
Take models from training runs to production endpoints with measurable quality and predictable latency.
- Distributed training
- Model serving
- RAG engineering
- Evaluation & monitoring
- MLOps/LLMOps
Cloud Platform Engineering
Build the deployment layer: landing zones, Kubernetes platforms, pipelines and cost governance across cloud and hybrid estates.
- Landing zones
- Kubernetes platforms
- Infrastructure as code
- CI/CD
- FinOps
Security & Governance Engineering
Engineer AI environments that operate inside enterprise trust boundaries with demonstrable control.
- Private AI deployment
- IAM & workload isolation
- Endpoint security
- Audit & compliance engineering
Solution & Application Engineering
Build the AI-native applications and integrations that put intelligence into business processes.
- Enterprise assistants
- Document AI
- Agentic workflows
- Enterprise search
- Full-stack development
Performance & FinOps Engineering
Profile, benchmark and govern AI workloads so performance and cost are both deliberate.
- GPU profiling
- Inference benchmarking
- Unit economics
- Rightsizing & placement
- Cost governance
Culture
How We Work
The culture that makes deep engineering work possible — and keeps it honest.
Engineering credibility first
We hire people who want their work to run in production — measured, operated and dependable — not just demonstrated.
Vendor-neutral by design
Our recommendations follow workloads, not vendor incentives. Engineers here develop breadth across ecosystems rather than allegiance to one.
Written thinking
Architecture decisions, trade-offs and post-mortems live in documents that outlast meetings. Clarity of writing is treated as an engineering skill.
Client-side presence
Our engineers work directly with client teams — from board-level strategy sessions to late-night production incidents.
Growing Teams
Where We're Growing
As demand for AI infrastructure expertise expands, these are the practice areas where we expect to grow our teams.
AI Infrastructure Engineering
GPU cluster, networking and scheduling specialists
Inference Performance Engineering
Serving optimization and benchmarking engineers
Private AI & Security Engineering
Secure deployment and governance engineers
Agentic Systems Engineering
Agent orchestration and tooling engineers
Cloud Platform Engineering
Kubernetes, IaC and FinOps engineers
Data & Retrieval Engineering
RAG, search and data pipeline engineers
Introduce Yourself.
We keep an open door for exceptional engineers. Tell us about the work you want to do and send your CV through the contact page.
Reach Out