At CACTUS, we are currently hiring an AI QA & Safety Engineer based in Delhi. This is an on-site, full-time employment opportunity.

Job responsibilities

Adversarial ML & Model Security Testing

- Design and execute adversarial attack campaigns against document intelligence, predictive analytics, fraud detection, computer vision, face recognition, and liveness detection systems
- Develop and maintain a reusable adversarial ML attack library and test harness for repeatable pre-production evaluation
- Produce adversarial-robustness reports for each pod service with quantitative measures, reproducible attack notebooks, and prioritised mitigation guidance

LLM, RAG & Agentic AI Red Teaming

- Lead structured red-team exercises against LLM, RAG and agentic AI deployments across the platform.
- Cover prompt injection, jailbreaks, indirect prompt injection via retrieved documents, data exfiltration, unsafe tool invocation, sandbox escape and policy-boundary violations by agents
- Develop and maintain red-team playbooks; publish anonymised playbooks and evaluation sets under standard metadata
- Advise pods on guardrail selection, output filtering, RAG source-integrity controls, retrieval provenance, and agent policy design

Hallucination, Calibration & Responsible AI Evaluation

- Design and run hallucination measurement, groundedness checks, and calibration/uncertainty evaluation for RAG systems and generative outputs across teams
- Run bias and fairness audits using quantitative metrics — demographic parity, equalised odds, calibration, subgroup accuracy.
- Conduct explainability evaluation (SHAP, LIME, Captum) and lightweight privacy impact assessments on team deliverables
- Author the technical safety evaluation content in model cards, dataset sheets, bias/hallucination/safety evaluation reports.

AI Security Architecture & Threat Modelling

- Own AI-specific threat modelling end-to-end for all pod systems under development — STRIDE, MITRE ATT&CK;, MITRE ATLAS and OWASP Top 10 for LLMs — including data pipelines, retrieval sources, model artefacts, prompt paths, tool interfaces and output surfaces
- Contribute AI security and Responsible AI requirements to RDRs and procurement documents.

Cross-Functional Team Advisory and Upskilling

- Advise each team’s AI QA Engineer on safety and Responsible AI test design, sample selection, evaluation metrics and evidence capture
- Brief and upskill teams on AI-specific security concerns and Responsible AI controls.

Research, Publication & Knowledge Transfer

- Track adversarial ML, LLM safety, agentic-AI safety and Responsible AI research literature; translate relevant findings into team-usable checks, controls and evaluation additions




- Maintain the platform’s open-source AI safety testing toolkit; deposit reusable notebooks, evaluation harnesses and playbooks, and publish reusable evaluation sets and safety artefacts under standard metadata for re-use.

Qualifications and prerequisites:

Educational Qualification

- B.Tech./B.E. or M.Tech./M.S./M.Sc. in Computer Science, Information Security, AI/ML, or a related quantitative discipline (Must have)
- Advanced degree (M.Tech./M.S./Ph.D.) with a thesis or published work in adversarial ML, AI security, LLM safety, or Responsible AI is highly desirable
- Certifications (Desirable): OSCP, GWAPT or CEH combined with demonstrable AI/ML security work; DeepLearning.AI or equivalent ML foundations; MLSecOps or LLM security specialist certifications where available
- Non-traditional backgrounds with demonstrable adversarial ML research, published safety work, credible LLM red-team disclosures, or CTF/red-team achievements will be considered in lieu of formal qualification

Work Experience

- 6+ years total in ML, applied AI, security research, or a closely related discipline; minimum 3 years specifically in adversarial ML, AI red teaming, LLM safety evaluation, or AI/ML security research
- Demonstrable hands-on LLM red-teaming experience with documented prompt injection, jailbreak, indirect-prompt-injection or agentic-tool-misuse campaigns against production or production-like systems
- Demonstrable adversarial ML work — evasion, model inversion, membership inference, model extraction, or data poisoning — against non-toy classifiers, vision models, or NLP systems
- Prior experience delivering safety, red-team, or Responsible AI work in BFSI, healthcare, or another regulated sector is a strong plus
- Prior experience advising or upskilling non-specialist engineering, security, or compliance teams on AI-specific security concerns is desirable

Technical Competencies

- Programming & ML Frameworks: Python (advanced); PyTorch or TensorFlow; Hugging Face Transformers; standard data-science tooling (NumPy, pandas, scikit-learn)
- Adversarial ML: Adversarial Robustness Toolbox (ART), Foolbox, CleverHans or equivalent; ability to implement custom attacks and defences; knowledge of certified robustness techniques
- LLM & Agentic AI Red Teaming: Demonstrable production-relevant experience with prompt injection, jailbreak,



indirect-prompt-injection, data-exfiltration, tool-misuse, sandbox-escape and multi-turn manipulation; familiarity with LLM guardrail frameworks (NeMo Guardrails, Guardrails AI, Llama Guard) and open red-team datasets
- Hallucination, Calibration & RAG Evaluation: RAGAS or equivalent; groundedness metrics, faithfulness scoring, retrieval quality metrics, calibration and uncertainty quantification; ability to build custom evaluation harnesses for RAG and generative pipelines
- Threat Modelling & AI Security Frameworks: STRIDE, MITRE ATT&CK;, MITRE ATLAS, OWASP Top 10 for LLMs, OWASP ML Top 10; ability to translate threat models into control specifications and test cases
- Explainability & Fairness: SHAP, LIME, Captum for model explainability; Fairlearn and AI Fairness 360 for fairness metrics; ability to design subgroup-fairness protocols for identity verification, fraud and predictive models
- Privacy-Enhancing Techniques: Working awareness of differential privacy, federated learning, PII redaction and anonymisation techniques; ability to run privacy impact assessments on model and data pipelines
- Communication & Advisory: Ability to author clear technical safety reports for a mixed engineering, architecture and executive audience; ability to brief and upskill non-AI security engineers and compliance colleagues; ability to represent the platform across various working groups

Additional information: If you are among the qualified candidates, one of our recruiters will contact you on phone or email with further details.

About Us

Established in 2002, Cactus Communications (cactusglobal.com) is a leading technology company that specializes in expert services and AI-driven products which improve how research gets funded, published, communicated, and discovered. Its flagship brand Editage offers a comprehensive suite of researcher solutions, including expert services and cutting-edge AI products like Mind the Graph, Paperpal, and R Discovery. With offices in Princeton, London, Singapore, Beijing, Shanghai, Seoul, Tokyo, and Mumbai and a global workforce of over 3,000 experts, CACTUS is a pioneer in workplace best practices and has been consistently recognized as a excellent place to work.

Awards and Recognition

- Employers of the Future, 2024
- Excellence in Employer Branding (Gold), 2024
- ISO 17100 certification for translation services, 2024
- Future of Workplace Disruptor, 2023
- Top 100 Companies for Remote Jobs (Ranked #14), 2023
- Three-star “Eruboshi” certification, 2023
- India’s Best Workplaces™ for Women (Top 100), 2022
- Quartz’s Best Companies for Remote Workers, 2022
- HR Asia’s Best Companies to Work for in Asia, 2021

📌 AI QA Engineer (Mumbai Suburban)
🏢 Cactus Communications Services Pte.
📍 Mumbai Suburban