Required Skills & Experience

Bachelor's Degree in Data Science, Statistics, Computer Science or Software Engineering 2+ years experience with Machine Learning Application Development 3+ years of software engineering experience

Nice to Have Skills & Experience

Master's Degree - Data Science, Statistics, Computer Science, or Software Engineering Machine Learning Operation Professional Certifications Demonstrated peer reviewed journal publications, conference presentations, open-source contributions, or similar activities. Strong knowledge of Adversarial Robustness techniques and tools for machine learning Strong knowledge of AI Risk Management frameworks and Trustworthy AI practices. Hands-on experience with applying statistics, machine learning algorithms (DNN, NLP), big data, and data science toolkits. Hands-on experience designing, implementing, and operationalizing high performant AI/ML pipelines and writing production code. Hands-on experience with deploying and operationalizing AI/ML models to public cloud environments. Hands-on experience evaluating open-source ML tools, frameworks, and libraries. Hands-on experience with commonly used data science programming languages, packages, and tools. Hands-on experience with MLOps, DevOps, DataOps and API integrations. Hands-on experience with AI workload management. Hands-on experience with Cloud architecture, design, implementation, and operations. Knowledge of application security controls (Web, API, Mobile, AI). Knowledge of security domains, common information security management and application frameworks: NIST 800-53, CSF, OWASP ASVS. Knowledge of Secure SDLC , Application Security design and DevSecOps Full stack knowledge of application architectures including: Single Page Applications, REST APIs, SOAP APIs, Mobile Applications. Experience with Java, Javascript and mobile application development. Knowledge or familiarity with database architectures including Oracle, SQL, DB2 and NoSQL Databases Experience with Cloud security, architecture, design, implementation, and operations Exposure to IAM Controls (OAuth 2.0, OIDC, JWT) Strong familiarity with Cryptography Controls (Data at rest, in motion). Certification - CISSP, CISM, CSSLP, CISA, CRISC

Job Description

Identify, analyze, and benchmark Generative AI augmented, LLM agentic security solutions in the market. Conduct proof-of-concept (PoC) assessments of selected cybersecurity capabilities to validate effectiveness in real-world environments. Define security control baselines and evaluation criteria for emerging risk security solutions. Evaluate vendor claims, solution architecture, and technical scalability. security testing of GenAI-powered cybersecurity tools. Publish detailed reports on the security, compliance, and efficacy of evaluated products.` Deliver and integrate AI robustness, vulnerability, and stress testing capabilities with MLOps ecosystems. Evaluate and assess open-source AI security libraries to build into enterprise AI stress testing and audit capabilities. Implement secure model development life cycle practices with automated white box and black box assessments for AI/ML models. Consistently enable strong developer and customer experience when liaising with application teams. Uphold Blue Box values when liaising with application teams.