Applied LLM Engineer - Prompts, Evals & Agents

The Role

We’re hiring an Applied LLM Engineer who lives and breathes prompt engineering and writes excellent production-grade Python. You’ll run a tight feedback loop with customers, turn real conversations into better prompts and eval datasets, and ship changes that measurably improve agent outcomes. This is a highly applied role working directly with customer feedback.

What You’ll Do

• Design & iterate prompts (system, tool/function-calling, task prompts) to boost voice AI agent success, reliability, and tone.
• Build co-pilots for customers to author their own prompts: meta-prompted assistants that suggest structures, lint for risks, autocomplete tool schemas, critique drafts, and generate eval cases.
• Work directly with customer feedback and conversation logs to identify failure modes; translate them into prompt changes, guardrails, and data improvements.
• Build eval datasets (success labels, rubrics, edge cases, regressions) and run offline/online evaluations (A/B tests, canaries) to quantify impact.
• Create Python utilities/services for prompt versioning, config-as-code, rollout/rollback, and guardrails (policies, refusals, redaction).
• Partner with PM/Success to define success metrics (task completion, first-pass accuracy, cost, latency) and instrument dashboards/alerts.
• Own LLM integration details: function/tool schemas, output parsing/validation (pydantic), retrieval-aware prompting, and fallback strategies.
• Ensure privacy & compliance (PII handling, anonymization, regional data boundaries) in datasets and logs.
• Share learnings via concise docs, playbooks, and internal demos.

Must-Have Skills

• Python: 3+ years writing clean, tested, production code (typing, pytest, profiling); experience building small services/APIs (FastAPI preferred).
• Prompt Engineering: Hands-on experience designing system/tool prompts, meta-prompting, rubric graders, and iterative prompt tuning based on real user data.
• LLM Integration: Comfortable with major APIs (OpenAI/Anthropic/Google/Mistral), function/tool calling, streaming, and robust output handling.
• Evaluation Mindset: Ability to define measurable success, create labeled datasets, and run methodical experiments/A/B tests.
• Product Sense: Comfortable talking with customers, turning qualitative feedback into shipped improvements.
• Data Hygiene: Practical experience cleaning, labeling, and balancing datasets; awareness of privacy/PII constraints.

Nice-to-Haves

• Experience building prompt-authoring UIs/SDKs or internal tooling for prompt versioning and governance.
• Agentic frameworks & tooling: DSpy, MCP, LangGraph, LlamaIndex, Rasa; experience with agent/tool schemas and orchestration.
• Observability & eval tooling: Langfuse, LangSmith, Braintrust; building eval harnesses and experiment dashboards.
• RAG & vector stores: Qdrant/Weaviate/Pinecone and retrieval-aware prompting.
• Experimentation workflows: A/B testing, prompt diffing/versioning.
• Infra & analytics: light SQL/log analysis, metrics & tracing, simple Grafana/OTel dashboards.
• Writing public blog posts or talks about applied LLM techniques.

Interview Process

• 30-min intro screen – background, role fit, questions both ways.
• Practical exercise (Prompt + Python) – design a prompt strategy, a customer-facing co-pilot flow, and a small eval harness.
• Team interviews – deep dive on product mindset, experimentation rigor, and collaboration.
• Founder/Leadership chat – scope, impact, and ways of working.

Why Join

• Own the reasoning layer and the customer co-pilot experience used at scale.
• Ship fast in a tight customer feedback loop and see your impact measured in days, not quarters.