✓Meaningful Equity: Every "Safestar" is a shareholder. You aren’t just an employee; you are a partner in our success.
✓Unlimited Leaves: We don’t believe in clock-watching. We offer unlimited leave because we trust you to take the time you need to recharge while staying committed to the mission.
✓Comprehensive Benefits: We provide top-tier medical insurance and wellness benefits to ensure you and your family are well cared for.
✓Career Trajectory: We are growing aggressively. For high-performers, the path for advancement moves at the speed of your ambition.
✓Build the AI platform: APIs, SDKs, and self-service workflows so AI/ML engineers deploy, version, evaluate, and monitor workloads without touching raw infrastructure.
✓Own GPU infrastructure: Cluster design, provisioning, scheduling, isolation, and utilisation optimisation across shared multi-team demand.
✓Run inference in production: vLLM, Triton, KServe, or Ray — continuous batching, autoscaling, model routing, and multi-model serving against real latency and throughput SLOs.
✓Optimize relentlessly: Drive tokens/sec, p99 latency, GPU utilization, and cost per token through quantization, batching strategy, and capacity planning.
✓Engineer GPU-aware Kubernetes: GPU Operator, device plugins, GPU-aware scheduling, MIG partitioning, and distributed workloads over NCCL.
✓Debug the hard layer: GPU OOMs, driver and CUDA runtime mismatches, interconnect bottlenecks, throughput regressions.
✓Build the MLOps backbone: Model registry and versioning, CI/CD for AI workloads, and safe rollout/rollback.
✓Own the evaluation layer: Offline and online eval harnesses, golden datasets, LLM-as-a-judge scoring, accuracy and hallucination metrics, and automated regression gates so no model, prompt, or quantisation change ships without a measured quality verdict.
✓Make it observable: Response quality and accuracy drift, latency, throughput, GPU utilisation, and per-tenant cost — with SLOs, alerting, and incident response.
✓Secure and multi-tenant by default: AuthN/AuthZ, secrets, data protection, tenant isolation, and governed access to models and GPUs.
✓Codify and lead: Terraform for everything, HA and DR designed in, plus architecture direction and mentoring across teams.
✓Contribute beyond AI: Design and build secure, multi-tenant microservices and APIs on AWS, run thorough code reviews, and own feature delivery end-to-end with Product and Design.
✓2-4 years building and operating production software and infrastructure, with senior ownership of systems end to end.
✓Bachelor's or Master's in Computer Science, Engineering, or equivalent practical experience.
✓Strong Python and/or Go — you write and review production services, not just scripts and manifests.
✓Deep Kubernetes and Docker: scheduling, resource management, operators, networking, debugging under load.
✓Strong Linux, networking, storage, and distributed systems fundamentals.
✓Production AWS/Azure/GCP experience — Lambda, API Gateway, EC2, S3, RDS and equivalents — with Terraform as your default way of working.
✓Working knowledge of SQL and NoSQL databases, including schema design and performance tuning.
✓Experience building and operating backend services and APIs in a multi-tenant SaaS product.
✓Leadership, code review, and mentoring skills, with end-to-end ownership of delivery in an agile environment.
✓Hands-on with a managed ML/AI platform — AWS SageMaker, Bedrock, GCP Vertex AI, or Azure ML — including where it fits and where self-managed infrastructure wins on cost or control.
✓Track record on HA, scalability, and DR in multi-tenant environments.
✓Solid observability and CI/CD practice — metrics, traces, SLOs, automated delivery.
✓Experience building or operating AI evaluation systems — accuracy and quality measurement, LLM-as-a-judge pipelines, eval datasets, and regression testing for models and prompts.
✓Background in platform engineering, infrastructure, SRE, distributed systems, AI infrastructure, or MLOps/LLMOps.
✓Hands-on GPU cluster operations: provisioning, capacity planning, and day-2 ownership.
✓NVIDIA ecosystem and CUDA — drivers, container runtime, toolkit compatibility, and their failure modes.
✓GPU scheduling, allocation, isolation, and utilisation optimisation across competing workloads.
✓Kubernetes GPU workloads: GPU Operator, device plugins, GPU-aware scheduling.
✓GPU troubleshooting and tuning: memory/OOM, driver faults, interconnect and throughput bottlenecks.
✓LLM inference and model serving in production (vLLM, Triton, KServe, Ray or equivalent) with demonstrated cost and performance gains.
✓Managed AI platform experience — SageMaker (training jobs, endpoints, inference components) or equivalent — alongside self-managed GPU serving.
✓AI teams deploy and roll back models through self-service workflows, with no manual infrastructure work per deployment.
✓GPU utilisation is measured, forecast, and consistently optimised across the shared fleet.
✓Cost per token falls quarter over quarter, with the numbers on a dashboard.
✓Inference SLOs hold under peak enterprise load, and GPU incidents drop in frequency and time-to-resolve.
✓No model, prompt, or optimisation change reaches production without passing automated accuracy and quality evaluation.
