Hire AI Engineersin 2 weeks
BearPlex AI engineers ship production AI systems end-to-end: combining LLM, ML, and platform engineering into one role. The hybrid generalist your AI roadmap actually needs. Embedded in your team in 14 days.
What a AI Engineer actually does at BearPlex
An AI engineer at BearPlex is the hybrid generalist that production AI roadmaps actually need: equally comfortable building an LLM agent system on Monday, training an XGBoost fraud model on Tuesday, and shipping the platform infrastructure to operate both on Wednesday. The role exists because most enterprise AI projects don't fit neatly into either 'pure LLM' or 'pure ML' boxes. A real customer support deployment needs RAG (LLM expertise), intent classification (classical ML), feature pipelines for analytics (data engineering), and serving infrastructure (platform). One engineer who can own all four ships dramatically faster than a team of specialists who have to coordinate across handoffs. AI engineers at BearPlex have backgrounds spanning LLM systems, classical ML, and platform engineering: they're the engineers we deploy when the project shape requires breadth, when team size is constrained, or when the client team needs a single technical owner across multiple AI initiatives.
Sample engineer profiles
Anonymized to respect engineer privacy. Full bios shared under NDA during scoping.
Owns end-to-end AI initiative for a Fortune 500: RAG-based knowledge system + ML-based churn prediction + custom serving platform. Single engineer, 14-month engagement.
Built hybrid AI system combining LLM agent + XGBoost risk model for legal document review: 73% reduction in attorney review time.
Shipped customer support AI for SaaS: RAG over docs + classification model for routing + analytics dashboard. 60% reduction in escalation rate.
Owns the BearPlex internal eval framework spanning LLM and classical ML evaluation: used across 11 active client engagements.
Skills matrix
The capabilities every BearPlex AI Engineer brings on day one.
| Skill | Proficiency | Typical tools |
|---|---|---|
| LLM systems (RAG, agents, fine-tuning) | Expert | LangGraph · Pinecone · Anthropic · OpenAI |
| Classical ML (XGBoost, LightGBM, scikit-learn) | Advanced | XGBoost · LightGBM · scikit-learn |
| Deep learning (PyTorch, TensorFlow) | Advanced | PyTorch · Hugging Face Transformers · PyTorch Lightning |
| Backend engineering (Python, TypeScript) | Expert | FastAPI · Next.js API routes · Django · Express |
| Data pipelines & feature engineering | Advanced | Airflow · Dagster · dbt · Polars |
| Production serving infrastructure | Expert | Modal · BentoML · vLLM · Vercel · AWS Lambda |
| Model evaluation (LLM + ML) | Expert | RAGAS · MLflow · LLM-as-judge · Custom golden datasets |
| Observability & monitoring | Advanced | LangSmith · Arize · Datadog · OpenTelemetry |
| Frontend integration (when needed) | Advanced | Next.js · React · Vercel AI SDK · Server-sent events |
| Multi-model orchestration | Expert | BearPlex Conductor pattern · LiteLLM · Custom routing |
| System design across LLM + ML + infra | Expert | Architecture diagrams · Trade-off analysis · Constraint engineering |
| Production debugging & incident response | Expert | Distributed tracing · Profiling · Database forensics |
How we vet AI engineers
Technical screen
60-minute call covering production AI experience across LLM and ML, system design across hybrid architectures, and a live debugging exercise. We're looking for engineers who can explain trade-offs across the full AI stack.
Live coding
2-hour paired session building a small hybrid AI pipeline: combining classical ML inference with LLM-based downstream processing. Constraints push on pragmatism (which library? which model size? when to fall back to heuristics?).
Systems design
90-minute design session on a production-realistic hybrid AI system (e.g., 'design a customer-service AI that uses ML for intent routing and LLM for response generation, serving 50K daily conversations with sub-2s p99'). We push on capacity planning, observability, failure modes, and graceful degradation.
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 (with appropriate isolation). Only if all four steps pass do they join the embedded pod.
What clients say
“We needed someone who could own our entire AI initiative end-to-end, not just one piece. BearPlex's AI engineer ships production code across LLM, ML, and infrastructure layers in a single sprint. We've never seen breadth like that.”
“Trying to build an AI system with three specialists turned into a coordination nightmare. BearPlex's AI engineer replaced all three and shipped faster than they did combined.”
“Our AI engineer became the technical owner of our AI roadmap. She maps strategic decisions to architectural ones and back, which is rare for an embedded engineer.”
Hiring AI engineers: questions answered
For many production AI initiatives, yes. The breadth-vs-coordination trade-off favors a single capable generalist over a team of specialists when the work spans 2-4 disciplines, when the team size budget is constrained, or when speed of decision-making matters. For very large initiatives requiring deep specialization in a single area, we'd add specialists alongside the AI engineer.
Per-person cost is similar to specialists. Total project cost is typically 30-50% lower because one engineer who owns end-to-end coordination eliminates handoff overhead, communication delays, and integration friction. The savings are biggest on smaller initiatives (2-6 month engagements).
Specialization in AI-specific patterns: production retrieval engineering, evaluation harness design, model monitoring and drift detection, sovereign deployment patterns, multi-model orchestration. Full-stack engineers can pick up AI capabilities but typically take 6-12 months to develop production AI judgment. Our AI engineers come pre-trained on AI-specific failure modes.
Most can, with TypeScript and React competence. They're not specialists in design systems or complex frontend state management: for that you'd want a dedicated frontend engineer. But for AI-product work where the frontend is mostly streaming chat UIs, dashboards, and integration with backend AI services, our AI engineers handle the full stack effectively.
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 tech stack, domain, and the specific blend of LLM/ML/platform work the role requires. 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 with another engineer at no cost. We've had to invoke this twice in 47 placements.
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.
Most BearPlex AI engineering engagements run 6-18 months. The shortest is a single 90-day War Room sprint for a focused build. The longest currently active is 30 months: same engineer, embedded full-time with the client's team.
All engineers sign individual NDAs with the client in addition to the BearPlex master agreement. They use the client's infrastructure (VPC, IAM, source control) where possible. Code written during the engagement belongs to the client. We never train models on client data without explicit written agreement.
Featured case studies
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