Hire Claude Developers in 2 weeks
BearPlex Claude developers build production applications on the Anthropic platform: Claude Sonnet, Opus, Haiku; Claude Agent SDK; MCP servers; tool use; prompt caching; computer use; extended thinking. Every BearPlex engineer works in Claude Code daily. Specialists in the Claude platform's idioms, strengths, and production patterns.
What a Claude developer actually does at BearPlex
A Claude developer at BearPlex builds production applications on the Anthropic platform end-to-end. They know the Claude API surface deeply: chat completions, tool use with parallel call support, prompt caching with 90% discount on cached prefixes, citations API for RAG-grounded outputs, extended thinking for hard reasoning, computer use for desktop application automation, and the Claude Agent SDK for production agent systems. They've shipped: production agent systems, code-generation features (Claude's strongest task category), customer support copilots, document analysis pipelines (long-context Claude shines here), and multi-step reasoning agents. They know when Claude is the right answer (code, long context, agents, safety-conscious enterprise) and when it isn't (image generation, cost-sensitive bulk classification, vendor-portable code). Equally important: they know Claude's idioms (XML structure for prompts, tool use ergonomics, prompt caching configuration) that produce significantly better results than generic LLM patterns. And they work in Claude Code daily, as does every engineer at BearPlex: agentic coding is our default delivery workflow, and we bring the same setup (custom skills, hooks, MCP connectors, CI integration) to client teams adopting Claude Code.
Sample engineer profiles
Anonymized to respect engineer privacy. Full bios shared under NDA during scoping.
Built a code-generation agent on Claude for a developer tools startup: generates production-quality API integrations from natural-language specs, used by 5K+ developers.
Shipped a long-document analysis system on Claude: handles 200K-token legal contracts with full citation tracking, deployed at a US AmLaw 100 firm.
Built a desktop automation agent using Claude's computer use capability: automates 47 distinct workflows across 3 legacy applications, eliminating 30 hours/week of manual work.
Designed a clinical decision support pipeline on Claude with extended thinking: medical entity extraction + reasoning over patient history; reviewed by clinicians at a US health system.
Skills matrix
The capabilities every BearPlex Claude developer brings on day one.
| Skill | Proficiency | Typical tools |
|---|---|---|
| Anthropic API (Messages, Tool Use) | Expert | anthropic SDK (Python, TS) · Tool use with parallel calls |
| Claude model selection (Sonnet, Opus, Haiku) | Expert | model routing patterns · cost-quality benchmarking |
| Claude Agent SDK production deployment | Expert | Claude Agent SDK · production agent patterns · HITL |
| Claude Code (agentic delivery workflow) | Expert | Claude Code · custom skills and hooks · CI integration |
| MCP server development | Expert | MCP SDKs (TS, Python) · internal-tool connectors · auth and pagination patterns |
| Prompt caching for cost optimization | Expert | cache_control markers · ephemeral vs persistent caching |
| Long-context Claude (200K, 1M) | Expert | long-context patterns · lost-in-the-middle awareness |
| Citations API for RAG | Expert | structured citation extraction · production RAG patterns |
| Extended thinking mode | Advanced | reasoning_effort tuning · complex reasoning patterns |
| Computer use (desktop automation) | Advanced | Claude computer use · automation patterns · screen interaction |
| Claude vision (multimodal) | Advanced | image input handling · multimodal prompting |
| Claude on AWS Bedrock | Expert | Bedrock API · BAA-compliant deployment · VPC integration |
| Claude on Vertex AI / Azure | Advanced | Vertex AI Anthropic models · platform-specific patterns |
| Multi-provider portability when needed | Expert | LiteLLM · Vercel AI SDK · provider-portable code |
How we vet Claude developers
Technical screen
60-minute deep-dive on past Claude platform work. We probe: model selection rationale (Sonnet vs Opus vs Haiku for which tasks), prompt caching usage, tool use design patterns, and what they learned from production. We screen out 'I just call the API' candidates: production Claude work has more depth.
Live Claude exercise
We give the candidate a realistic Claude integration problem with quality, cost, and latency constraints, and 90 minutes. They must architect the solution, write production code with tool use and prompt caching, and discuss trade-offs.
Architecture interview
Whiteboard a production Claude-based system for a realistic scenario: multi-step agent with tool use, fine-tuned context for high-volume path, prompt caching for cost optimization. We probe for: routing logic, evaluation, monitoring, fallback patterns.
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 Claude developer cut our Anthropic bill 65% in 4 weeks by implementing prompt caching properly. Same product quality, much lower cost.”
“We thought we knew Claude. The BearPlex developer surfaced patterns we weren't using: Claude Agent SDK instead of our custom agent loop, computer use for legacy app automation, citations API replacing our hand-rolled citation parsing. Game-changing.”
“Production Claude work is full of details: tool use reliability patterns, prompt caching configuration, model selection per task. The BearPlex developer brought 6 years of these scars; our system actually works because of it.”
Hiring Claude developers: questions answered
Yes. We run Claude Code across our whole engineering organization, so adoption help comes from practice rather than a slide deck: workspace and permission setup, custom skills and hooks for your repo conventions, MCP servers that connect Claude Code to your internal tools, CI integration, and the review discipline that keeps agent-written code at production quality. A typical adoption engagement pairs a short enablement sprint with an embedded engineer who stays until the velocity gains stick.
Yes: MCP (Model Context Protocol) server development is core Claude platform work for us. We've shipped production MCP servers, including a roughly 100-tool CRM server for our own SaaS product, with proper auth, pagination, and confirmation gates on destructive operations. If your goal is Claude working against your real systems, whether from Claude Code or from an API agent, an MCP server is usually the first thing to build.
Claude developers specialize in the Anthropic platform deeply (Claude Agent SDK, prompt caching, tool use, computer use, extended thinking). LLM engineers cover broader LLM systems and are typically multi-provider. For Claude-committed engagements, Claude developers are the right hire; for multi-provider work, LLM engineers are.
Yes: most production Claude work happens in mixed environments. Our Claude developers know the Anthropic platform deeply and are also competent with OpenAI (GPT-4o, GPT-5) and increasingly Google (Gemini 2.5). The right answer for production is provider-portable code that uses whichever model wins on each specific task.
Yes: common for clients with cloud-specific requirements. Claude on AWS Bedrock supports BAA arrangements for HIPAA. Claude on Vertex AI integrates with Google Cloud IAM and security controls. We've deployed Claude across all three platforms (Anthropic API, Bedrock, Vertex AI) and know the operational differences.
Claude Agent SDK provides cleaner production agent ergonomics: tool use handling, parallel calls, prompt caching, computer use, HITL patterns. For production agent systems, the SDK saves significant engineering work. For simple single-shot calls, raw SDK is sufficient.
Yes: increasingly common for clients with legacy desktop applications. Claude computer use is the most production-ready desktop automation capability available. We've shipped automation systems that handle data entry, legacy app workflows, and complex GUI interactions where API access wasn't available.
Architecturally. Stable prompt prefix (system prompt, retrieved documents) goes into the cached portion; variable suffix (user message) goes after. Anthropic's 90% discount on cached prefixes is the largest cost lever for production Claude applications: typical applications see 50-70% total cost reduction with proper cache structure.
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.
Limited: Anthropic doesn't offer general public fine-tuning of Claude models. AWS Bedrock has private preview for some Claude variants. For most cases requiring fine-tuning, we recommend distilling Claude outputs into a fine-tuned open-source model (Llama 3.3 or Mistral with LoRA): same task quality at fraction of cost for high-volume workloads.
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