Educational Requirements

- Bachelor of Engineering

Service Line
- Topaz COE unit

Responsibilities
- 1.Execute Responsible AI Evaluations:Conduct structured assessments on fairness, safety, robustness, explainability, and hallucination risks for models under development or deployment.
- 2.Design Evaluation Datasets and Metrics:Create curated, adversarial, and edge-case datasets and define quantitative metrics for evaluating fairness, toxicity, reliability, and ethical alignment.
- 3.Support Guardrail and Control Implementation:Contribute to implementing technical guardrails, safety filters, explainability modules, and automated checks within AI pipelines.
- 4.Analyze Ethical and Technical Risks:Review datasets, model outputs, and system behavior to identify fairness gaps, robustness issues, transparency deficiencies, and other Responsible AI risks.
- 5.Participate in Red Teaming and Stress Testing:Assist with scenario-based adversarial evaluations, prompt safety checks, robustness tests, and model vulnerability analysis.
- 6.Support Deployment of Responsible AI Workflows:Assist in implementing lifecycle governance workflows, templates, and processessuch as via IBM OpenPages or equivalent governance tooling.
- 7.Prepare Transparency and Governance Documentation:Develop model cards, system cards, evaluation reports, risk logs, and supporting documentation required for governance reviews and audit readiness.
- 8.Assist in Continuous Monitoring:Support creation of dashboards, metrics,



and monitoring signals to track fairness drift, hallucination patterns, model instability, and safety deviations.
- 9.Collaborate Across Engineering and Governance Functions:Work closely with AI engineers, data scientists, product teams, legal, ISG, DPO, and governance bodies to ensure Responsible AI requirements are consistently applied.
- 10.Assist in Training and Knowledge Enablement:Help develop training content, guides, and resources to educate internal teams on Responsible AI evaluation methods, guardrails, and governance expectations.

Technical and Qualified Requirements:
- Proficiency in Python and ML/DL frameworks (PyTorch, TensorFlow) for evaluation and experimentation.
- Understanding of fairness libraries (Fairlearn, AIF360) and ability to compute ethics related evaluation metrics.
- Familiarity with explainability tools (SHAP, LIME, Captum, Integrated Gradients).
- Exposure to red teaming concepts, prompt safety evaluation, and data integrity checks.
- Experience with MLOps basics including evaluation pipelines, experiment tracking, and CI workflows.
- Understanding of ML algorithms, generative models, and supervised/unsupervised learning techniques.
- Familiarity with NLP, vision, speech, and structured data domains.
- Knowledge of datasets, benchmark suites, and third party model ecosystems.

Preferred Skills:
- Technology->AI-Generative AI->Generative AI - Basic->retrieval augmented generation (rag)
- Technology->AI-Responsible AI->Responsible AI

📌 Sr. Responsible AI Analyst (Bengaluru)
🏢 Infosys
📍 Bengaluru