Hire LLM Engineersin 2 weeks
BearPlex LLM engineers build production language model systems (agents, RAG pipelines, fine-tuned deployments) for Fortune 500s and high-growth scale-ups. We embed engineers into your team in 14 days.
What a LLM Engineer actually does at BearPlex
An LLM engineer at BearPlex owns the full lifecycle of a production language model system. That means designing the prompt and retrieval architecture, building the evaluation harness BEFORE writing the agent loop, integrating with your existing data sources and IAM, hardening for production with proper observability (LangSmith, Arize, OpenTelemetry), and operating the system after launch. Our LLM engineers ship to production within the first sprint of an engagement: they don't write demos that get thrown away. They've worked with the full stack: GPT-5, Claude Sonnet 4.5, Llama 3.3, fine-tuning with LoRA and DPO, RAG with Pinecone/Qdrant/Weaviate, agent frameworks like LangGraph and the Claude Agent SDK, and the operational tooling that distinguishes a prototype from a system you can run at scale. They also know what NOT to build: they'll push back on architecture decisions that feel sophisticated but won't survive production.
Sample engineer profiles
Anonymized to respect engineer privacy. Full bios shared under NDA during scoping.
Shipped a 12-tool autonomous agent for Fortune 100 logistics: handles 47 distinct workflows with 95%+ task completion.
Built a citation-tracked RAG system over 4M+ legal documents for a US AmLaw 100 firm: zero hallucination incidents in 18 months production.
Fine-tuned a Llama 3.3 70B variant for multilingual healthcare clinical NLP: deployed sovereign in client's HIPAA-bounded VPC.
Owns the BearPlex internal evaluation harness: RAGAS + custom golden datasets + LLM-as-judge running on 11 client engagements.
Skills matrix
The capabilities every BearPlex LLM Engineer brings on day one.
| Skill | Proficiency | Typical tools |
|---|---|---|
| Prompt engineering & system prompting | Expert | Anthropic console · OpenAI playground · PromptFoo · Custom test harnesses |
| RAG architecture & retrieval | Expert | Pinecone · Qdrant · Weaviate · pgvector · BM25 hybrid |
| Agent design (LangGraph, CrewAI, Claude Agent SDK) | Expert | LangGraph · CrewAI · AutoGen · Claude Agent SDK |
| LLM fine-tuning (LoRA, QLoRA, DPO) | Advanced | PyTorch · Hugging Face TRL · Axolotl · Unsloth |
| Evaluation & observability | Expert | RAGAS · LangSmith · Arize · Weights & Biases |
| Production inference (vLLM, TGI, serverless) | Advanced | vLLM · TGI · Modal · Anyscale · Together.ai |
| Sovereign deployment (on-prem, air-gapped) | Advanced | AWS Bedrock · Azure OpenAI · GCP Vertex · On-prem GPU clusters |
| Multi-model orchestration | Expert | BearPlex Conductor pattern · LiteLLM · OpenRouter |
| Cost optimization (caching, smaller models for triage) | Advanced | Helicone · Anthropic prompt caching · Smaller models for routing |
| Security & guardrails | Advanced | Guardrails AI · NeMo Guardrails · Lakera · Custom prompt injection defense |
| Frontend integration (streaming, tool calls) | Working knowledge | Vercel AI SDK · Server-sent events · WebSockets |
| TypeScript / Python (production code) | Expert | TypeScript · Python 3.11+ · Pydantic · FastAPI |
How we vet LLM engineers
Technical screen
60-minute call covering production LLM experience, system design, and a live debugging exercise on a real (sanitized) BearPlex codebase. We're looking for engineers who can explain trade-offs, not just demonstrate facts.
Live coding
2-hour paired session building a small RAG pipeline from scratch with constraints (no LangChain, must handle access control, must implement evaluation). We watch for code organization, debugging instincts, and architectural judgment.
Systems design
90-minute design session on a production-realistic AI system (e.g., 'design a multi-tenant RAG for a SaaS company with 10K customer organizations'). We push on capacity planning, security, observability, and failure modes.
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
“BearPlex's LLM engineer was operating in our codebase like an internal team member by week two. Most contractors take a quarter to get there.”
“We've worked with three vendors to build agentic systems. BearPlex was the only one who shipped to production. The others are still iterating on prototypes.”
“Their LLM engineer pushed back on our original RAG architecture and proposed something simpler. Three months later, the simpler version is what's running in production.”
Hiring LLM engineers: questions answered
Specialization in production LLM patterns: retrieval engineering (chunking, hybrid search, reranking, citation tracking), agent design with proper state management, evaluation engineering with golden datasets and LLM-as-judge, sovereign deployment with cost optimization, and security patterns specific to LLMs (prompt injection, jailbreaks, data exfiltration). They've worked through these problems in production, not just read about them.
Our minimum engagement is 6 months at 50%+ allocation. We've found smaller engagements don't allow the engineer to build sufficient context to be effective. If you need a bounded project, our Single Service engagement model (4-12 weeks, fixed-price) is the better fit.
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 team culture. Days 8-14 the engineer reads your codebase, sets up local dev, attends standups as observer, 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, and async written handoff for the rest of the day.
Yours. We work with whatever you already have: OpenAI, Anthropic, AWS Bedrock, Azure OpenAI, open models, your choice of vector DB, your existing observability stack. We push back when an architectural choice will hurt you in production, but we're not vendor-aligned.
Most BearPlex LLM 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.
Yes: under NDA we can share sanitized BearPlex internal frameworks (evaluation harness, agent orchestration patterns, RAG reference implementation). Several BearPlex engineers also contribute to public open-source projects we'll point you to.
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
Get matched with a LLM Engineer in 14 days
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