Hire Prompt Engineersin 2 weeks

Significant overlap, different specialties. LLM engineers own the full system architecture (RAG pipeline, agent orchestration, model selection, infrastructure). Prompt engineers go deep on the prompts themselves: design, evaluation, iteration, monitoring. On a typical BearPlex engagement: 1 LLM engineer for system architecture + 1 prompt engineer for prompt + eval lifecycle + 1 MLOps engineer for production operations.

Yes: model portability is a core skill. Our engineers know Claude's XML preferences, GPT's JSON mode reliability, Gemini's long-context strengths, and the open-source model quirks (Llama, Mistral, Qwen). They design prompts that translate across providers when possible and fork when necessary: useful for cost arbitrage and provider-redundancy patterns.

With evaluation harnesses, not vibes. Standard tooling: Promptfoo for prompt-level CI, Braintrust or LangSmith for production trace analysis, custom rubric-based LLM-as-judge for subjective tasks, golden datasets for regression detection. Every meaningful prompt change is measured against the eval suite before shipping.

Yes: prompt versioning and lifecycle management is a core capability. We've helped clients consolidate hundreds of ad-hoc prompts into versioned prompt libraries with eval coverage, A/B testing, and rollback. The infrastructure question is as important as the prompt content question at scale.

Yes: increasingly important for client-facing AI. We design adversarial prompt suites covering OWASP LLM Top 10 categories: prompt injection, jailbreaking, sensitive information disclosure, model denial of service. For high-stakes deployments (financial, healthcare, legal), red-team evaluation is part of every release cycle.

For a single high-stakes prompt with full eval coverage: 1-3 weeks. The prompt itself is often v1 in a day; the work is in defining the evaluation, gathering test cases, iterating against measured failures, and instrumenting for production monitoring. Rushed prompts without eval coverage are how you ship silent regressions.

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.

If you have any of: (1) more than 5 prompts in production with no version control or evaluation, (2) silent quality regressions that you only notice from user complaints, (3) inconsistent prompt patterns across teams, (4) AI features where stakeholders disagree on whether outputs are 'good,' (5) prompts that worked on Day 1 but degrade as model versions change. These are the symptoms of needing a dedicated prompt engineering function.