Note: The job is a remote job and is open to candidates in USA. OVA.Work is seeking a Responsible AI Engineer to design, implement, and operationalize responsible AI practices across the AI development lifecycle. The role focuses on improving AI system safety, fairness, transparency, and compliance while collaborating with various teams to ensure trustworthy and ethical AI solutions.
Responsibilities
- Design and implement Responsible AI frameworks, processes, and engineering practices
- Evaluate AI and machine learning systems for fairness, bias, transparency, safety, and reliability
- Develop methods to detect and mitigate algorithmic bias and unintended model behavior
- Perform AI risk assessments, model evaluations, and impact assessments
- Implement explainability and interpretability techniques for machine learning models
- Develop testing frameworks for AI robustness, adversarial risks, and model safety
- Evaluate Large Language Models (LLMs) and Generative AI applications for hallucinations, harmful outputs, security risks, and reliability issues
- Build AI safety guardrails, content filtering, and responsible generation controls
- Conduct red teaming, adversarial testing, and failure analysis for AI systems
- Establish monitoring practices for model drift, fairness, performance, and compliance
- Collaborate with engineering teams to integrate Responsible AI controls into MLOps pipelines
- Create documentation including model cards, data sheets, risk assessments, and AI governance reports
- Stay updated on AI regulations, industry standards, and emerging Responsible AI practices
Skills
- Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, Machine Learning, Ethics in Technology, Cybersecurity, or a related field
- 3+ years of experience in machine learning engineering, AI evaluation, AI governance, data science, or related fields
- Strong understanding of machine learning algorithms, AI lifecycle management, and model evaluation
- Experience with Python and machine learning frameworks
- Knowledge of Responsible AI concepts including fairness, explainability, transparency, privacy, and safety
- Experience analyzing AI model performance and identifying risks or failure patterns
- Strong analytical, problem-solving, and documentation skills
- Experience evaluating Large Language Models (LLMs), Generative AI, and AI agents
- Experience with AI safety testing, red teaming, and adversarial machine learning
- Knowledge of fairness and explainability tools such as IBM AI Fairness 360, Fairlearn, SHAP, LIME, or InterpretML
- Experience with AI evaluation frameworks such as DeepEval, Ragas, LangSmith, Promptfoo, or OpenAI Evals
- Familiarity with AI governance frameworks and regulatory requirements
- Experience implementing Responsible AI practices within MLOps pipelines
- Knowledge of privacy-preserving machine learning techniques
Company Overview