Senior LLM Prompt Engineer

📌 Job Title: LLM / Prompt Engineer – AI Stewardship Squad

Location: Remote from LATAM | Full-time contractor
Company: Inallmedia.com

🚀 About the Role

As part of Inallmedia's AI Stewardship Squad, you'll be responsible for turning state-of-the-art LLMs—like GPT‑4‑turbo, Claude 3, or Llama 3—into production-ready, measurable features. Your mission will include selecting or fine-tuning foundation models, implementing scalable Retrieval-Augmented Generation (RAG) pipelines, versioning prompts, and evangelizing best practices in prompt engineering across product teams.

You’ll also play a key role in ensuring model reliability, safety, and auditability through prompt monitoring, bias mitigation, and secure deployment workflows.

🎯 Key Responsibilities

• 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

🧠 Ideal Candidate

• 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
• Comfortable working with LLMs in production environments using CI/CD and containerization
• Exposure to red teaming techniques for foundational model testing

🛠️ Recommended Stack

• LLMs: GPT‑4/5, Claude, Gemini, Perplexity
• Frameworks: LangChain, LlamaIndex, Hugging Face Transformers
• Vector DBs: Pinecone, Weaviate, Milvus
• Languages: Python
• DevOps: GitHub Actions, GitLab CI, Docker, Kubernetes (basic for deployment)
• Monitoring & Evaluation: OpenAI Evals, TruLens, Prometheus, OpenTelemetry
• Infra: Azure, AWS, GCP

☁️ Infrastructure & Environment

• 100% remote across LATAM
• GitHub/GitLab for code and prompt versioning
• CI/CD pipelines and Terraform-based IaC
• Secure VPN/VPC access with MFA
• Focus on AI safety: input validation, RBAC, encryption-at-rest