Hire MLOps Engineersin 2 weeks
BearPlex MLOps engineers build the infrastructure that turns ML and LLM systems from notebooks into reliable production services: pipelines, monitoring, CI/CD, drift detection, deployment.
What a MLOps Engineer actually does at BearPlex
An MLOps engineer at BearPlex builds the production infrastructure that ML and LLM systems need to operate reliably: distinct from data science (modeling) and ML engineering (model development). Their work is the platform layer: data pipelines (Airflow, Dagster, Prefect), model registries and versioning (MLflow, Weights & Biases), CI/CD for ML (testing, validation, blue-green deployment), serving infrastructure (BentoML, Seldon, SageMaker, vLLM for LLMs), monitoring for data drift and model decay (Evidently, Arize, WhyLabs), feature stores when warranted (Feast, Tecton), and the operational discipline that distinguishes a working model from a system you can run for years. They handle both classical ML systems (where model decay over months is the concern) and LLM systems (where prompt changes, model version updates, and cost optimization are the concerns). Our MLOps engineers have shipped MLOps platforms for fraud detection running on millions of daily transactions, recommendation systems with hourly retraining cycles, LLM applications serving thousands of concurrent users, and the meta-platforms (CI/CD for ML, feature stores) that ML teams build their work on top of.
Sample engineer profiles
Anonymized to respect engineer privacy. Full bios shared under NDA during scoping.
Built the MLOps platform for a Fortune 500 retailer: 23 production models, automated retraining triggered by drift detection, full audit trail for model decisions.
Owns the production LLM serving infrastructure for a healthcare AI startup: 99.95% uptime, sub-200ms p99 latency, integrated drift monitoring across 8 deployed models.
Designed feature store architecture for a B2B SaaS: 200+ features, 8 production models consuming, sub-50ms feature serving for online inference.
Built end-to-end CI/CD pipeline for ML: automated testing, model validation gates, blue-green deployment, automated rollback on production metric regression.
Skills matrix
The capabilities every BearPlex MLOps Engineer brings on day one.
| Skill | Proficiency | Typical tools |
|---|---|---|
| Pipeline orchestration (Airflow, Dagster, Prefect) | Expert | Apache Airflow · Dagster · Prefect · Temporal |
| Model registry & experiment tracking | Expert | MLflow · Weights & Biases · DVC · Neptune |
| Model serving (online + batch) | Expert | BentoML · Seldon Core · SageMaker endpoints · Triton Inference Server · vLLM for LLMs |
| Feature store design (when warranted) | Advanced | Feast · Tecton · Vertex AI Feature Store · Custom on Postgres |
| CI/CD for ML (testing, validation, deployment) | Expert | GitHub Actions · GitLab CI · Jenkins · ArgoCD |
| Model monitoring & drift detection | Expert | Evidently AI · Arize · WhyLabs · Custom dashboards |
| LLM-specific ops (prompt versioning, cost tracking) | Expert | LangSmith · Helicone · Custom prompt registries |
| Infrastructure as code (Terraform, Pulumi) | Expert | Terraform · Pulumi · AWS CDK |
| Container orchestration (Kubernetes, ECS) | Advanced | Kubernetes · AWS ECS · GKE · Helm |
| Cloud ML platforms (SageMaker, Vertex AI, Azure ML) | Expert | AWS SageMaker · GCP Vertex AI · Azure ML |
| Sovereign deployment (on-prem GPU, air-gapped) | Advanced | On-prem Kubernetes · Custom inference servers · Air-gapped CI/CD |
| Cost optimization & autoscaling | Expert | GPU autoscaling · Spot instances · Inference batching · Cost dashboards |
How we vet MLOps engineers
Technical screen
60-minute call covering production MLOps experience, pipeline design, monitoring strategy, deployment patterns. We're looking for engineers who've operated production ML systems through real incidents.
Live coding
2-hour paired session building a small ML pipeline end-to-end: orchestration, model serving, monitoring, basic CI/CD. We watch for production thinking, observability instincts, and operational pragmatism.
Systems design
90-minute design session on a production-realistic MLOps platform (e.g., 'design CI/CD for 50 models across 6 teams with model validation gates and automated rollback'). We push on team scalability, governance, and incident response.
Reference check + paid trial work
We talk to two prior managers or technical peers. The engineer then completes 1-2 days of paid sample work on a real BearPlex client engagement. Only if all four steps pass do they join the embedded pod.
What clients say
“We had a working ML model and no infrastructure to operate it. BearPlex's MLOps engineer built the entire platform (pipelines, monitoring, CI/CD, deployment) in 12 weeks. Three years later it's still our backbone.”
“Most MLOps consultants build platforms that look great in demos but break under real load. BearPlex's engineer built for our actual production volume from day one. Best technical hire we've made for ML infrastructure.”
“Our internal team had been building MLOps for a year and were stuck. BearPlex's engineer rebuilt the architecture in six weeks and we measured a 60% reduction in time-to-production for new models.”
Hiring MLOps engineers: questions answered
Yes: modern MLOps spans both. Classical ML systems (with their concerns about feature stores, drift, retraining) and LLM systems (with their concerns about prompt versioning, model API updates, cost optimization, evaluation pipelines). Our engineers handle both fluently, though with different patterns for each.
When you have multiple production ML/LLM systems, when model versioning and lineage matter for compliance, when you need feature stores or model monitoring, when retraining pipelines must be automated, or when CI/CD for ML differs significantly from CI/CD for application code. These are the signals that ML platform work is its own discipline.
Yes. We work with whatever you've adopted: SageMaker, Vertex AI, Databricks, Azure ML, custom platforms. We push back when an architectural choice will hurt you in production, but we're not platform-aligned. For greenfield projects we have opinions, but they're recommendations, not mandates.
14 days from initial intake to embedded. Day 0 is a 60-minute scoping call. Days 1-7 we match an engineer based on your platform stack, scale, and the specific MLOps challenges. Days 8-14 the engineer reads your codebase, sets up local dev, attends standups, and starts shipping by end of week 2.
21 days from start. If the engineer isn't a fit during the first 21 days, you don't pay for their time and we replace them at no cost. We've had to invoke this twice in 47 placements.
Most BearPlex MLOps engagements run 9-18 months. Platform building takes time. Shorter engagements (90-day War Room sprints) work for focused builds (single pipeline, single platform component) but full platform work typically requires sustained ownership.
Most can in a pinch, but model development is not their primary specialization. For systems where you need both platform building AND model development, we typically pair an MLOps engineer with an ML engineer in the same pod. Each goes deep on their discipline.
Primarily Lahore, Pakistan (HQ) with client-facing presence in Austin and Doha. Time zone overlap with US clients is 5-9 hours; we structure engagements with daily 2-3 hour overlap windows for synchronous work, async handoff for the rest.
Compliance-aware MLOps is a core part of our work for regulated industries. Our engineers know the operational requirements (audit logging, access controls, encryption at rest/in transit, change management, evidence collection) and design platforms that pass examiner review without retrofitting.
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