LLM / Prompt Engineer

In All Media

full-time

Posted on: 9/11/2025

Origin: • 🇨🇷 Costa Rica

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Mid-LevelSenior

AWSAzureDockerGoogle Cloud PlatformKubernetesPrometheusPythonTerraform

About the role

Select, fine-tune, or deploy LLMs for product-specific needs
Design and optimize RAG architectures with observability and vector search integration
Implement and monitor prompt versioning using version control and CI/CD pipelines
Track and analyze key metrics: hallucination rate, grounding score, latency, token cost
Detect and mitigate bias in prompts (cultural, gender, linguistic)
Support red teaming activities to assess model robustness and safety
Collaborate with MLOps and platform teams to deploy models in production environments
Promote internal standards for LLMOps, prompt governance, and model observability
Ensure model reliability, safety, and auditability through prompt monitoring and secure deployment workflows

2–4 years of hands-on experience with LLMs and Retrieval-Augmented Generation (RAG)
5–8 years in machine learning or software engineering roles
Proven experience building RAG pipelines with LangChain or equivalent frameworks
Familiarity with vector databases such as Pinecone, Weaviate, or Milvus
Strong understanding of prompt lifecycle management: versioning, evaluation, rollback
Experience implementing metrics and monitoring pipelines: hallucination rate, grounding, token usage
Demonstrated ability to detect and reduce prompt-related bias (cultural, gender, linguistic)
Comfortable working with LLMs in production environments using CI/CD and containerization
Exposure to red teaming techniques for foundational model testing
Recommended stack: GPT‑4/5/Claude/Gemini, LangChain/LlamaIndex/Hugging Face, Python
DevOps familiarity: GitHub Actions, GitLab CI, Docker, Kubernetes (basic)
Monitoring & evaluation tools: OpenAI Evals, TruLens, Prometheus, OpenTelemetry