Data Science Engineer

Role Focus : Production ML Systems | GPU Orchestration | Inference at Scale What You'll Actually Do (Not Buzzwords) Infrastructure That Doesn't Break Design and maintain the backbone for training, fine-tuning, and deploying ML models that actually work in production Orchestrate GPU workloads on Kubernetes (EKS) with node autoscaling, intelligent bin-packing, and cost-aware scheduling (spot instances, preemptibles—you know the drill) Build CI / CD pipelines that handle ML code, data versioning, and model artifacts like a well-oiled machine (GitHub Actions, Argo Workflows, Terraform) Production ML, Not Science Projects Partner with Data Scientists and ML Engineers to turn Jupyter notebooks into production-grade systems Deploy and scale inference backends (vLLM, Hugging Face, NVIDIA Triton) that serve real traffic Optimize GPU utilization because every idle A100 hour is money burning Build observability that actually tells you why things broke (Prometheus, Grafana, OpenTelemetry) Ship Fast, Sleep Well Create tooling for seamless model deployment, instant rollback, and A / B testing Lead incident response when production AI systems decide to have opinions Work with security and compliance teams to implement best practices without slowing down innovation What We're Really Looking For Must-Haves (No Negotiation) 5+ years in MLOps, infrastructure, or platform engineering —you've been in the trenches Production ML experience : At least one project that's serving real users, not a Kaggle competition Kubernetes expertise with GPUs : You understand taints, tolerations, affinity rules, and why GPU scheduling is its own special hell Cloud-native architecture (AWS preferred) : You think in VPCs, IAM roles, and cost optimization Training pipeline experience : Set up or scaled training / fine-tuning for ML models in production (PyTorch Lightning, Hugging Face Accelerate, DeepSpeed) IaC fluency : Terraform, Helm, Kustomize are second nature Python engineering skills : You can debug a distributed training failure and fix it Inference scaling : You've deployed and scaled inference workloads and lived to tell the tale The "We're Very Interested" Signals You mention scaling inference and we can see the fire in your eyes You've used MLflow, W&B, or SageMaker Experiments and have opinions on which is best You understand CI / CD for ML and why it's different from regular software You've built monitoring systems that caught issues before users did Nice to Have (But Seriously Nice) GPU scheduling wizardry in Kubernetes Model drift monitoring and versioning tools Low-latency inference optimization (quantization, FP8, TensorRT—the good stuff) Experience in compliance or regulated industries where "just ship it" isn't an option What Makes This Role Different Ownership. You're not a ticket-taker or a consultant passing through. You'll own infrastructure that powers real AI products, make architectural decisions that matter, and have the autonomy to build things the right way. Impact. Your work directly affects model training speed, inference latency, GPU costs, and system reliability. You'll see the results of your optimizations in dollars saved and milliseconds gained. Quality over speed. We value security, operational excellence, and sustainable systems. No "move fast and break things" chaos here—we move deliberately and build things that last. The Reality Check This role is not for you if : You prefer working on proofs-of-concept over production systems You think "it works on my machine" is an acceptable answer You haven't shipped ML systems to production You're looking for pure research or pure DevOps (this is the intersection) This role is for you if : You get excited about making GPUs go brrr efficiently You've been oncall for ML systems and learned hard lessons You believe infrastructure is a product, not an afterthought You want to build the foundation for AI that actually works Write to to get connected!