Hire AI Platform Engineersin 2 weeks
BearPlex AI platform engineers build the internal AI infrastructure other product teams build on: shared model serving, retrieval infrastructure, evaluation pipelines, governance frameworks, developer experience. The platform that lets your product teams ship AI features without rebuilding foundations.
What a AI Platform Engineer actually does at BearPlex
An AI platform engineer at BearPlex builds the infrastructure that powers all AI initiatives across an organization. The role spans: shared model serving infrastructure (frontier models, fine-tuned models, self-hosted open-source), centralized retrieval infrastructure (vector indexes, reranking, embedding pipelines), model governance and registry (version tracking, validation, MRM integration), evaluation infrastructure (golden datasets, LLM-as-judge pipelines, regression detection), developer experience (internal SDKs, documentation, templates), cost monitoring and optimization, and the operational layer that makes the platform reliable. They work across the modern AI infrastructure stack: vLLM and Triton for serving, Pinecone and Qdrant for retrieval, MLflow and custom registries for governance, Promptfoo and Braintrust for evaluation, LangSmith and Helicone for observability. They've built platforms supporting 5-50+ AI initiatives across organizations and know what scales vs what doesn't.
Sample engineer profiles
Anonymized to respect engineer privacy. Full bios shared under NDA during scoping.
Built the AI platform for a Series D SaaS: supports 18 production AI features across 6 product teams; cut cost-per-AI-feature 60% through shared infrastructure.
Designed the AI platform for a top-20 US bank: supports 12 production initiatives, examiner-defensible governance, integrates with existing MRM and IAM.
Built an edge-first AI platform for a global ecommerce client: sub-200ms p95 globally, supports 8 product teams, costs 40% less than the previous centralized architecture.
Stood up the platform that supported a healthcare AI startup through Series A → B growth: handled 10× scale increase without architectural rewrites.
Skills matrix
The capabilities every BearPlex AI Platform Engineer brings on day one.
| Skill | Proficiency | Typical tools |
|---|---|---|
| Shared model serving infrastructure (vLLM, Triton, TGI) | Expert | vLLM · Triton Inference Server · Hugging Face TGI |
| Managed model integration (Bedrock, Azure OpenAI, Vertex AI) | Expert | AWS Bedrock · Azure OpenAI · Google Vertex AI |
| Centralized retrieval infrastructure | Expert | Pinecone · Qdrant · Weaviate · pgvector · shared embedding pipelines |
| Model governance and registry | Expert | MLflow Model Registry · custom registries · MRM integration |
| Evaluation infrastructure (centralized eval pipelines) | Expert | Promptfoo · Braintrust · Inspect · custom CI integration |
| Internal SDK design and developer experience | Expert | Python SDKs · TypeScript SDKs · documentation, templates, examples |
| Production observability and cost tracking | Expert | LangSmith · OpenTelemetry · Helicone · Prometheus · custom dashboards |
| Compliance-aware platform design | Expert | NIST AI RMF integration · audit logging · MNPI segregation patterns |
| Multi-cloud and sovereign deployment | Advanced | AWS, Azure, GCP · on-prem GPU clusters · BAA-compliant deployment |
| GPU cluster management | Advanced | Kubernetes · Slurm · Ray · GPU sharing patterns |
| Cost optimization (caching, routing, distillation) | Expert | Prompt caching infrastructure · model routing · distillation pipelines |
| Platform team leadership and roadmap | Advanced | Stakeholder management · platform-as-product mindset |
How we vet AI platform engineers
Technical screen
60-minute deep-dive on past AI platform work. We probe: did the platform serve real product teams? What patterns scaled vs what didn't? How did the platform evolve as new use cases emerged? We screen out engineers whose 'platform' was a single proof-of-concept.
Live design exercise
We give the candidate a realistic AI platform problem (Series C SaaS with 5 AI initiatives, OCC 2011-12 bank with 8 initiatives) with constraints, and 90 minutes. They must design the platform foundations, identify the top 3 architectural decisions, and discuss trade-offs.
Platform-engineering interview
Whiteboard the developer experience for a realistic scenario. We probe for: SDK design, documentation strategy, governance integration, cost tracking, and how the platform supports product teams without imposing unnecessary constraints.
Reference checks + paid trial
Two engineering reference checks plus a 21-day paid trial on a real client engagement. We don't take engineers off trial until both Hamad and the client engineer report 'I want this person on the team next sprint.'
What clients say
“Their platform engineer designed the infrastructure that lets our 6 product teams ship AI features in days instead of months. The shared evaluation infrastructure alone has prevented dozens of regressions.”
“Best AI platform work I've seen. The compliance integration was the killer feature: every product team gets MRM-aligned governance for free, not as an extra hoop.”
“We were skeptical that platform engineering ROI would be visible. Three months in, our cost-per-AI-feature was down 60% and shipping velocity was up 4×. Pays for itself.”
Hiring AI platform engineers: questions answered
Build a platform when you have 5+ AI initiatives where shared infrastructure would help. Below that, per-project infrastructure is often appropriate. The platform investment pays back when initiatives multiply: 10+ AI features sharing infrastructure is dramatically more efficient than 10 features each rebuilding foundations.
Yes: governance integration is a core part of AI platform work. We've built platforms aligned with NIST AI RMF, OCC 2011-12 / SR 11-7 model risk management, EU AI Act compliance, ISO 42001 certification preparation, and sector-specific regulations. The platform makes compliance automatic for product teams, not an extra hoop.
Yes: recommended approach. Standard pattern: ship the foundations first (shared serving, basic retrieval), get the first 2-3 product teams using the platform, evolve based on real usage. Platforms built without real users tend to over-engineer the wrong things; we ship for actual product teams from day one.
Initial platform foundation engagement: $300K-$1M (16-24 weeks) depending on scope. Ongoing platform development: $1M-$3M annually for a dedicated 4-8 person platform team. Single AI platform engineer embedded: $40K-$60K monthly retainer. The platform investment is significant but pays back across all AI initiatives.
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.
Yes: most enterprise platforms support both. Managed APIs (Claude via Bedrock with BAA, OpenAI via Azure, Gemini via Vertex AI) for highest-quality use cases. Self-hosted open-source (Llama, Mistral, Qwen via vLLM) for cost-sensitive or sovereignty-required use cases. The platform's routing layer abstracts the choice from product teams while enforcing governance per model type.
Yes: designed for it. Standard pattern: platform engineer designs and ships foundations alongside the client's existing engineering team, with explicit knowledge transfer throughout. By month 12-18, the client team owns the platform; BearPlex transitions to advisory or expansion role.
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