Zensar Technologies—AI Engineer

Job description

Description: Gemini – AssureAI Engineer 2 positions Keywords: AssureAI · Evaluation Datasets · Red-Teaming · Bias & Explainability (SHAP/LIME) · Threshold Gates · CI/CD · Audit Evidence · Python About the Role You are one of two hands-on operators of AssureAI's Trustworthiness pillar inside the Governance Control Tower. Where the AI Trust & Compliance Engineer sets the strategy — which regulations map to which checks, what a passing threshold means — you build and run the evaluation suites that prove it, day in and day out, across a growing roster of Gemini-based agents. You report to the AI Trust & Compliance Engineer and work closely with the AI Engineers building the agents you test. What You'll Own - Build and maintain evaluation datasets and scenarios for each of AssureAI's 19 Trustworthiness checks (explainability, audit provenance, regulation coverage, safety guardrails) as new agents come online. - Run scheduled and on-commit bias, toxicity, red-team, and explainability (SHAP/LIME) suites; triage failures and route them to the right owner (AI Engineer, Integration Specialist, or Governance Engineer). - Maintain the CI/CD threshold-gate configuration so a failing Trustworthiness check blocks release rather than just flagging it. - Package audit evidence — run provenance, evidence exportability, regulation-coverage reports — for the AI Trust & Compliance Engineer's CSG review-board submissions. - Track dataset coverage and flag gaps as agent scope expands into new domains, data types, or regulatory contexts. - Split coverage with the second AssureAI Engineer across agent domains or pipeline stages so evaluation throughput scales with agent count. What We're Looking For - 5-7 years in QA/test engineering for ML or GenAI systems, ideally with a dedicated eval framework (DeepEval, Ragas, Promptfoo, or comparable). - Hands-on experience with AssureAI or a directly comparable AI-assurance/evaluation platform. - Working knowledge of bias/fairness testing, explainability techniques (SHAP/LIME), and red-teaming/adversarial-prompt methodology. - Comfortable reading AI regulatory requirements (EU AI Act, NIST AI RMF, ISO/IEC 42001) well enough to translate them into test coverage. - Proficient in Python; comfortable wiring test suites into CI/CD (Cloud Build, GitHub Actions). - Detail-oriented and comfortable owning a queue of failing checks across multiple agents at once. Nice to Have - Experience with Gemini Enterprise / ADK agents specifically, or another enterprise agent platform. - Familiarity with RAG evaluation (faithfulness, hallucination, context precision/recall). - Prior audit or compliance-adjacent work (SOC 2, ISO 27001, or similar) that makes evidence packaging second nature. What Success Looks Like By month two: every live agent has an active Trustworthiness evaluation suite running on every commit, with clear pass/fail thresholds. By month four: audit-evidence packages are produced on a standing cadence without ad hoc requests, and dataset coverage gaps are tracked and closed as new agents onboard. Requirements: Gemini – AssureAI Engineer 2 positions Keywords: AssureAI · Evaluation Datasets · Red-Teaming · Bias & Explainability (SHAP/LIME) · Threshold Gates · CI/CD · Audit Evidence · Python About the Role You are one of two hands-on operators of AssureAI's Trustworthiness pillar inside the Governance Control Tower. Where the AI Trust & Compliance Engineer sets the strategy — which regulations map to which checks, what a passing threshold means — you build and run the evaluation suites that prove it, day in and day out, across a growing roster of Gemini-based agents. You report to the AI Trust & Compliance Engineer and work closely with the AI Engineers building the agents you test. What You'll Own - Build and maintain evaluation datasets and scenarios for each of AssureAI's 19 Trustworthiness checks (explainability, audit provenance, regulation coverage, safety guardrails) as new agents come online. - Run scheduled and on-commit bias, toxicity, red-team, and explainability (SHAP/LIME) suites; triage failures and route them to the right owner (AI Engineer, Integration Specialist, or Governance Engineer). - Maintain the CI/CD threshold-gate configuration so a failing Trustworthiness check blocks release rather than just flagging it. - Package audit evidence — run provenance, evidence exportability, regulation-coverage reports — for the AI Trust & Compliance Engineer's CSG review-board submissions. - Track dataset coverage and flag gaps as agent scope expands into new domains, data types, or regulatory contexts. - Split coverage with the second AssureAI Engineer across agent domains or pipeline stages so evaluation throughput scales with agent count. What We're Looking For - 5-7 years in QA/test engineering for ML or GenAI systems, ideally with a dedicated eval framework (DeepEval, Ragas, Promptfoo, or comparable). - Hands-on experience with AssureAI or a directly comparable AI-assurance/evaluation platform. - Working knowledge of bias/fairness testing, explainability techniques (SHAP/LIME), and red-teaming/adversarial-prompt methodology. - Comfortable reading AI regulatory requirements (EU AI Act, NIST AI RMF, ISO/IEC 42001) well enough to translate them into test coverage. - Proficient in Python; comfortable wiring test suites into CI/CD (Cloud Build, GitHub Actions). - Detail-oriented and comfortable owning a queue of failing checks across multiple agents at once. Nice to Have - Experience with Gemini Enterprise / ADK agents specifically, or another enterprise agent platform. - Familiarity with RAG evaluation (faithfulness, hallucination, context precision/recall). - Prior audit or compliance-adjacent work (SOC 2, ISO 27001, or similar) that makes evidence packaging second nature. What Success Looks Like By month two: every live agent has an active Trustworthiness evaluation suite running on every commit, with clear pass/fail thresholds. By month four: audit-evidence packages are produced on a standing cadence without ad hoc requests, and dataset coverage gaps are tracked and closed as new agents onboard. Responsibilities: Gemini – AssureAI Engineer 2 positions Keywords: AssureAI · Evaluation Datasets · Red-Teaming · Bias & Explainability (SHAP/LIME) · Threshold Gates · CI/CD · Audit Evidence · Python About the Role You are one of two hands-on operators of AssureAI's Trustworthiness pillar inside the Governance Control Tower. Where the AI Trust & Compliance Engineer sets the strategy — which regulations map to which checks, what a passing threshold means — you build and run the evaluation suites that prove it, day in and day out, across a growing roster of Gemini-based agents. You report to the AI Trust & Compliance Engineer and work closely with the AI Engineers building the agents you test. What You'll Own - Build and maintain evaluation datasets and scenarios for each of AssureAI's 19 Trustworthiness checks (explainability, audit provenance, regulation coverage, safety guardrails) as new agents come online. - Run scheduled and on-commit bias, toxicity, red-team, and explainability (SHAP/LIME) suites; triage failures and route them to the right owner (AI Engineer, Integration Specialist, or Governance Engineer). - Maintain the CI/CD threshold-gate configuration so a failing Trustworthiness check blocks release rather than just flagging it. - Package audit evidence — run provenance, evidence exportability, regulation-coverage reports — for the AI Trust & Compliance Engineer's CSG review-board submissions. - Track dataset coverage and flag gaps as agent scope expands into new domains, data types, or regulatory contexts. - Split coverage with the second AssureAI Engineer across agent domains or pipeline stages so evaluation throughput scales with agent count. What We're Looking For - 5-7 years in QA/test engineering for ML or GenAI systems, ideally with a dedicated eval framework (DeepEval, Ragas, Promptfoo, or comparable). - Hands-on experience with AssureAI or a directly comparable AI-assurance/evaluation platform. - Working knowledge of bias/fairness testing, explainability techniques (SHAP/LIME), and red-teaming/adversarial-prompt methodology. - Comfortable reading AI regulatory requirements (EU AI Act, NIST AI RMF, ISO/IEC 42001) well enough to translate them into test coverage. - Proficient in Python; comfortable wiring test suites into CI/CD (Cloud Build, GitHub Actions). - Detail-oriented and comfortable owning a queue of failing checks across multiple agents at once. Nice to Have - Experience with Gemini Enterprise / ADK agents specifically, or another enterprise agent platform. - Familiarity with RAG evaluation (faithfulness, hallucination, context precision/recall). - Prior audit or compliance-adjacent work (SOC 2, ISO 27001, or similar) that makes evidence packaging second nature. What Success Looks Like By month two: every live agent has an active Trustworthiness evaluation suite running on every commit, with clear pass/fail thresholds. By month four: audit-evidence packages are produced on a standing cadence without ad hoc requests, and dataset coverage gaps are tracked and closed as new agents onboard.