[Remote] AI/LLM Evaluation & Alignment Software Engineer

Descrição da Vaga

Requisitos

• Bachelor's or Master's in Computer Science, Artificial Intelligence, Data Science, or related field
• 3–5+ years of hands-on experience in ML/AI engineering, with at least 2 years working directly on LLM evaluation, QA, or safety
• Strong familiarity with evaluation techniques for generative AI: human-in-the-loop evaluation, automated metrics, adversarial testing, red-teaming
• Experience with bias detection, fairness approaches, and responsible AI design
• Knowledge of LLM observability, monitoring, and guardrail frameworks e.g Langfuse, Langsmith
• Proficiency with Python and modern AI/ML/LLM/Agentic AI libraries (LangGraph, Strands Agents, Pydantic AI, LangChain, HuggingFace, PyTorch, LlamaIndex)
• Experience integrating evaluations into DevOps/MLOps pipelines, preferably with Kubernetes, Terraform, ArgoCD, or GitHub Actions
• Understanding of cloud AI platforms (AWS, Azure) and deployment best practices
• Strong problem-solving skills, with the ability to design practical evaluation systems for real-world, high-stakes scenarios
• Excellent communication skills to translate technical risks and evaluation results into insights for both technical and non-technical stakeholders

Responsabilidades

• Build and maintain evaluation frameworks for LLMs and generative AI systems tailored to public safety and intelligence use cases
• Design guardrails and alignment strategies to minimize bias, toxicity, hallucinations, and other ethical risks in production workflows
• Partner with AI engineers and data scientists to define online and offline evaluation metrics (e.g., model drifts, data drifts, factual accuracy, consistency, safety, interpretability)
• Implement continuous evaluation pipelines for AI models, integrated into CI/CD and production monitoring systems
• Collaborate with stakeholders to stress test models against edge cases, adversarial prompts, and sensitive data scenarios
• Research and integrate third-party evaluation frameworks and solutions; adapt them to our regulated, high-stakes environment
• Work with product and customer-facing teams to ensure explainability, transparency, and auditability of AI outputs
• Provide technical leadership in responsible AI practices, influencing standards across the organization
• Contribute to DevOps/MLOps workflows for deployment, monitoring, and scaling of AI evaluation and guardrail systems (experience with Kubernetes is a plus)
• Document best practices and findings, and share knowledge across teams to foster a culture of responsible AI innovation

Benefícios