Dl Lead qa engineer Jobs – 34 Open Jobs Today

AI Safety Researcher
Expertshub.ai —Delhi, Delhi
- Computer Science
- Master's degree
- Bachelor's degree
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Senior Software Engineer - 1630
GHX —Delhi, Delhi
- Computer Science
- B.Sc
- System design
Senior Automation Engineer (Java + Selenium) -AI
SITA Switzerland Sarl —Delhi
- Full-time
- Hybrid work
- Computer Science
- Java
- SQL
- Work from home
AI Product Manager
Techmagnate —Delhi, Delhi
- Azure
- AWS
- Analysis skills
Senior Software Developer - Full Stack
ThrivePass —Delhi, Delhi
- Full-time
- Hybrid work
- Azure
- DevOps
- System design
Principal Engineer (Web)
Lucideus —Unknown, Delhi
- Computer Science
- Master's degree
- Bachelor's degree
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Software Developer
KVH Industries, Inc. —New Delhi District, Delhi
- Full-time
- Computer Science
- B.Sc
- Salesforce
Software Developer
Crypto Mize —Delhi
- Full-time
- Oracle
- English
- Java
iOS Developer
Crypto Mize —Delhi
- Full-time
- English
- Java
- APIs

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AI Safety Researcher

Expertshub.ai -
Delhi, Delhi

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Job details

Contractual / Temporary

Qualifications

TensorFlow
Image processing
Law
Computer Science
GWAPT
NumPy
Information security
Master's degree
CEH
Pandas
Information Security
Bachelor's degree
M.Sc
APIs
B.E.
AI
Python

Full job description

AI Safety Researcher

ROLE OVERVIEW
The AI Safety Researcher will lead adversarial testing, LLM and agentic AI red teaming, model-security assessment, Responsible AI evaluation, and AI-specific threat modelling across government AI programmes. The role combines applied research with repeatable testing, technical advisory, governance evidence, and open-source knowledge transfer.

Educational Qualifications

B.Tech./B.E. or M.Tech./M.S./M.Sc. in Computer Science, Information Security, AI/ML, or a related quantitative discipline is required.
An advanced degree with thesis or published work in adversarial ML, AI security, LLM safety, or Responsible AI is highly desirable.
Desirable certifications include OSCP, GWAPT, CEH with demonstrable AI/ML security work, ML foundations, MLSecOps, or LLM-security credentials.
Candidates with strong adversarial ML research, safety publications, credible LLM red-team disclosures, or CTF/red-team achievements may be considered in lieu of conventional qualifications.

Experience

6+ years in ML, applied AI, security research, or a related discipline, including at least 3 years in adversarial ML, AI red teaming, LLM safety evaluation, or AI/ML security research.
Demonstrable LLM red-teaming experience covering prompt injection, jailbreaks, indirect prompt injection, data exfiltration, and agentic tool misuse in production or production-like systems.
Hands-on adversarial ML experience involving evasion, model inversion, membership inference, model extraction, or data poisoning against non-trivial models.
Experience in government, BFSI, healthcare, or another regulated sector is a strong advantage.
Experience advising and upskilling engineering, security, compliance, or programme teams on AI-specific risks is desirable.

Key Responsibilities

Adversarial ML and Model Security: design and execute attack campaigns covering evasion, inversion, membership inference, extraction, and poisoning against document intelligence, predictive analytics, fraud, computer vision, face-embedding, and liveness systems.
Develop and maintain reusable adversarial ML attack libraries, evaluation harnesses, reproducible notebooks, quantitative robustness measures, and prioritised mitigation guidance.
LLM, RAG, and Agentic AI Red Teaming: lead structured exercises covering jailbreaks, direct and indirect prompt injection, retrieved-document attacks, data exfiltration, unsafe tool invocation, sandbox escape, multi-turn manipulation, and policy-boundary violations.
Develop red-team playbooks and anonymised evaluation sets for government AI use cases, and advise teams on guardrails, output filtering, source integrity, retrieval provenance, and agent policy design.
Hallucination and Responsible AI Evaluation: measure hallucination, groundedness, faithfulness, calibration, uncertainty, retrieval quality, bias, subgroup performance, and fairness.
Conduct explainability evaluation using SHAP, LIME, or Captum, and perform lightweight privacy impact assessments for model and data pipelines.
Author safety content for model cards, dataset sheets, bias and hallucination reports, safety evaluations, and Responsible AI evidence aligned with relevant Indian government frameworks.
AI Security Architecture and Threat Modelling: own AI-specific threat models using STRIDE, MITRE ATT&CK, MITRE ATLAS, OWASP Top 10 for LLMs, and OWASP ML guidance across data, model, retrieval, prompt, tool, and output surfaces.
Review deployment patterns across sovereign/on-premise GPU environments, IndiaAI Compute, and CSP APIs; recommend defensive controls and architecture patterns.
Contribute AI-security and Responsible AI requirements to RFQs, RDRs, procurement documents, and agency evaluations.
Advise AI QA Engineers and delivery teams on safety test design, sampling, metrics, evidence capture, and remediation priorities.
Track current research and translate relevant findings into practical controls, tests, playbooks, and programme standards.
Maintain open-source safety toolkits and publish reusable notebooks, evaluation harnesses, playbooks, and datasets to approved government repositories.

Technical Competencies

Advanced Python; PyTorch or TensorFlow; Hugging Face Transformers; NumPy, pandas, and scikit-learn.
Adversarial ML frameworks such as ART, Foolbox, CleverHans, or equivalent, including custom attack and defence implementation.
Production-relevant LLM and agentic AI red teaming, including guardrail frameworks such as NeMo Guardrails, Guardrails AI, or Llama Guard.
RAG and generative evaluation using RAGAS or equivalent, groundedness, faithfulness, retrieval metrics, calibration, and uncertainty quantification.
Threat modelling with STRIDE, MITRE ATT&CK, MITRE ATLAS, OWASP Top 10 for LLMs, and OWASP ML Top 10.
Explainability and fairness using SHAP, LIME, Captum, Fairlearn, and AI Fairness 360.
Working knowledge of differential privacy, federated learning, PII redaction, anonymisation, and privacy impact assessment.
Knowledge of IndiaAI Safe & Trusted AI, MeitY Responsible AI and security guidance, CERT-In directions, and DPDPA 2023.
Ability to produce clear technical reports and brief mixed engineering, architecture, compliance, and executive audiences.

Work Location: In person

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lead qa engineer jobs in dl

AI Safety Researcher

Senior Software Engineer - 1630

Senior Automation Engineer (Java + Selenium) -AI

AI Product Manager

Senior Software Developer - Full Stack

Principal Engineer (Web)

Software Developer

Software Developer

iOS Developer

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