Responsible AI Engineer

We are seeking a Responsible AI Engineer to design, implement, and operationalize responsible AI practices across the AI development lifecycle. The ideal candidate will focus on improving AI system safety, fairness, transparency, explainability, robustness, and compliance. This role collaborates with AI Engineers, Data Scientists, Machine Learning Engineers, Security Teams, Legal Teams, and Product Managers to ensure AI solutions are trustworthy, ethical, and aligned with organizational and regulatory requirements., * 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.

• 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.

Preferred Qualifications

• 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.

Technical Skills

• Python
• SQL
• Scikit-learn
• PyTorch
• TensorFlow
• Pandas
• NumPy
• SHAP
• LIME
• InterpretML
• Fairlearn
• AI Fairness 360
• Large Language Models (LLMs)
• Prompt Engineering
• AI Evaluation Frameworks
• MLflow
• Kubeflow
• Docker
• Git
• REST APIs
• AWS / Azure / Google Cloud Platform

Responsible AI Expertise

• AI fairness and bias mitigation
• Explainable AI (XAI)
• Model interpretability
• AI safety evaluation
• Adversarial testing
• AI risk assessment
• Model documentation
• Data governance
• Privacy-preserving AI
• Human-in-the-loop AI systems
• AI transparency and accountability

Soft Skills

• Strong ethical reasoning and analytical judgment
• Excellent communication skills across technical and non-technical teams
• Ability to translate AI risks into practical engineering solutions
• Strong attention to detail
• Collaboration and stakeholder management skills
• Continuous learning mindset

Nice to Have

• Experience with enterprise AI governance programs
• Knowledge of AI regulations and standards
• Experience with LLM safety, jailbreak testing, and prompt injection defense
• Familiarity with AI security and privacy practices
• Experience creating AI policies, standards, and governance documentation
• Publications or research experience in AI safety, fairness, or trustworthy AI

Key Performance Indicators (KPIs)

• Reduction in AI fairness and safety risks
• AI model evaluation coverage
• Effectiveness of bias detection and mitigation processes
• Reduction in harmful or unreliable AI outputs
• Responsible AI compliance readiness
• Quality of AI risk documentation and governance artifacts
• Adoption of Responsible AI practices across AI projects
• Improvement in AI transparency and user trust metrics