The engineers who make AI safe in production.
Evals, offline/online benchmarks, guardrails, observability, model routing, prompt versioning and inference cost controls — the LLMOps discipline your pilots need before they hit prod.
- 48 hrs — 3 vetted profiles delivered
- Evals & benchmarks
- Guardrails & safety
- Observability & cost
30 minutes, no slides. A senior delivery lead reviews your stack and gives you a concrete pilot outline.
- LangSmith
- Arize
- Langfuse
- AWS Bedrock
- Azure OpenAI
A working pilot in 90 days — not a 40-page slide deck.
Golden datasets, LLM-as-judge rubrics, deterministic regression suites and pre-prod release gates.
Trust Layer, Guardrails AI, prompt-injection defenses, PII redaction and red-teaming pipelines.
LangSmith / Arize / Langfuse tracing, model routing, prompt versioning and per-workload cost controls.
“Their LLMOps engineer stood up our eval harness in two weeks — we caught a quality regression that would have shipped otherwise.”
Questions buyers ask us first.
- What backgrounds do LLMOps engineers have?
- Senior platform / MLOps engineers with 5+ years shipping ML systems, plus recent hands-on eval and guardrail work in production.
- Which tooling do they use?
- LangSmith, Arize, Langfuse, Weights & Biases, plus custom eval harnesses and Bedrock/Azure model routers.
- Contract or contract-to-hire?
- Both — most engagements start as 12-week embedded contracts with option to convert.
- Which observability and eval stacks do they operate?
- LangSmith, Langfuse, Arize, Weights & Biases and OpenTelemetry — wired into CI/CD with automated evals, drift detection and cost dashboards.
Ready to see it in your stack?
30 minutes with a delivery lead. Your architecture, your KPIs, a concrete pilot outline you can defend internally.
Book a 30-min working session →