Are you passionate about building safe and trustworthy AI? Hackajob is partnering with Leo Technologies to hire a Responsible AI Engineer who will design, test, and deploy evaluation systems for Large Language Models (LLMs) and generative AI. As a Responsible AI Engineer, you’ll play a key role in developing guardrails, ensuring fairness, and implementing ethical safeguards for AI solutions used in public safety and intelligence. If you have strong ML/AI experience and a commitment to responsible AI practices, this is your opportunity to shape the future of high-impact AI systems.
Responsible AI Engineer Responsibilities
As a Responsible AI Engineer, you will:
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Build and maintain evaluation frameworks for LLMs and generative AI systems tailored to public safety and intelligence use cases
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Design guardrails and alignment strategies to reduce bias, toxicity, hallucinations, and other ethical risks
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Define and implement online/offline evaluation metrics (accuracy, consistency, interpretability, safety, model/data drifts)
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Develop continuous evaluation pipelines integrated with CI/CD and production monitoring systems
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Stress test models against adversarial prompts, edge cases, and sensitive data scenarios
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Research and integrate third-party evaluation frameworks, adapting them to regulated, high-stakes environments
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Partner with customer-facing teams to ensure explainability, transparency, and auditability of AI outputs
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Provide technical leadership in responsible AI practices and influence organization-wide standards
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Contribute to DevOps/MLOps workflows for AI evaluation (Kubernetes experience is a plus)
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Document best practices and share knowledge to foster responsible AI innovation
Responsible AI Engineer Requirements
To succeed as a Responsible AI Engineer, you should have:
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Bachelor’s or Master’s in Computer Science, AI, Data Science, or related field
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3–5+ years of ML/AI engineering experience, including 2+ years in LLM evaluation, QA, or safety
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Expertise in generative AI evaluation techniques: automated metrics, human-in-the-loop testing, adversarial testing, and red-teaming
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Experience with bias detection, fairness approaches, and responsible AI design
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Knowledge of LLM observability and monitoring tools (Langfuse, Langsmith)
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Proficiency in Python and libraries such as LangGraph, Strands Agents, Pydantic AI, LangChain, HuggingFace, PyTorch, and LlamaIndex
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Experience integrating evaluations into DevOps/MLOps workflows (Kubernetes, Terraform, ArgoCD, GitHub Actions)
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Familiarity with cloud AI platforms (AWS, Azure) and best practices for deployment
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Strong problem-solving skills to design real-world AI evaluation systems
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Excellent communication skills to translate technical findings for technical and non-technical audiences
Why Join as a Responsible AI Engineer?
As a Responsible AI Engineer, you’ll work at the intersection of cutting-edge AI technology and ethical responsibility. Your work will directly influence how AI is deployed in high-stakes environments, ensuring safety, fairness, and trust. You’ll have the chance to collaborate with talented teams while leading efforts in building transparent, auditable, and responsible AI systems.