Senior Consultant/Manager | Cyber Operate | AI/ML Engineer (Cyber Automation) | KSA

About Deloitte: When you work for us, you commit to a career at one of the largest and most prestigious professional services firms in the world. We have received numerous awards over the last few years, including Best Employer in the Middle East, and Best Consulting Firm, and the Middle East Training & Development Excellence Award. Our Purpose Deloitte makes an impact that matters. Every day we challenge ourselves to do what matters most—for clients, for our people, and for society. We serve clients distinctively, bringing innovative insights, solving complex challenges and unlocking sustainable growth. We inspire our talented professionals to deliver outstanding value to clients, providing an exceptional career experience and an inclusive and collaborative culture. We contribute to society, building confidence and trust in the markets, upholding the integrity of organizations and supporting our communities. Our shared values guide the way we behave to make a positive, enduring impact: During your tenure as a Senior Consultant/ Manager, you will demonstrate and develop your capabilities in the following areas Find and prioritise opportunities • Work with domain leads to map manual, repetitive and high-volume tasks across strategy, technical assessments, risk, compliance, TPRM, governance and performance management • Build a prioritised backlog of AI and automation use cases, with expected time savings, risk and effort for each • Track AI advancements and good practice, and share them with the Innovation Guidance Team and the wider programme Build AI and automation solutions • Design, build and deploy solutions such as: • Evidence and document analysis: reviewing policies, contracts, SOC 2 reports and compliance evidence against required controls • TPRM automation: pre-filling and reviewing supplier questionnaires and flagging gaps • Vulnerability analytics: enriching and prioritising findings using asset, threat and exploit data • Reporting automation: generating draft assessment reports, dashboards and executive summaries • AI assistants: helping the team search frameworks, policies and past work • Anomaly detection: detection use cases with the SOC where relevant • Build data pipelines and integrations with security tools, GRC and TPRM platforms, ticketing systems and Power BI • Choose the right approach for each problem, whether rules, scripts, classical ML or large language models (LLMs), rather than defaulting to AI Make AI secure and responsible • Define standards for safe AI use in the programme: data handling, human review of AI outputs, prompt and model security, and logging • Protect sensitive data: keep it within approved environments and meet PDPL, NCA and SDAIA requirements • Test AI solutions against prompt injection, data leakage and incorrect outputs, following OWASP Top 10 for LLM Applications and NIST AI RMF • Apply MLOps practices: version control, testing, monitoring, model performance tracking and documented governance of each solution Enable the team • Document solutions clearly and train specialists to use them well • Measure adoption and time saved, and report results to programme leadership with the Performance Management team Leadership Capabilities • Builds own understanding of our purpose and values; explores opportunities for impact. • Demonstrates strong commitment to personal learning and development; acts as a brand ambassador to help attract top talent. • Understands expectations and demonstrates personal accountability for keeping performance on track. • Actively focuses on developing effective communication and relationship-building skills. • Understands how their daily work contributes to the priorities of the team and the business.  Qualifications • Total years of experience: 3-7 total professional years. • Bachelor's or Master's in computer science, data science, AI or a related field • Strong Python, with experience shipping ML or LLM-based solutions into real use, not only prototypes • Experience with ML and LLM frameworks (e.g. PyTorch, scikit-learn, LangChain, LlamaIndex or similar) and building retrieval-augmented (RAG) solutions • Experience with cloud AI platforms (Azure OpenAI / Azure AI, AWS Bedrock / SageMaker or GCP Vertex AI) and APIs • Data engineering skills: SQL, pandas and building data pipelines • Understanding of secure and responsible AI practices • Cybersecurity, GRC or risk domain experience • Experience integrating with ServiceNow, Archer, OneTrust or security tool APIs • MLOps tooling (MLflow, Docker, CI/CD) • Knowledge of Saudi AI and data regulations (SDAIA AI Ethics Principles, PDPL) • Arabic language is a plus. • At least one preferred: Microsoft Azure AI Engineer (AI-102), AWS Certified Machine Learning (Specialty or Engineer), Google Professional Machine Learning Engineer. Also valued: ISO/IEC 42001 Lead Implementer, IAPP AIGP. • NIST AI RMF 1.0 • ISO/IEC 42001 • SDAIA AI Ethics Principles • OWASP Top 10 for LLM Applications • PDPL • NCA ECC-2:2024 Show more Show less

Total years of experience: 3-7 total professional years. Bachelor's or Master's in computer science, data science, AI or a related field. Strong Python, with experience shipping ML or LLM-based solutions into real use, not only prototypes. Experience with ML and LLM frameworks (e.g. PyTorch, scikit-learn, LangChain, LlamaIndex or similar) and building retrieval-augmented (RAG) solutions. Experience with cloud AI platforms (Azure OpenAI / Azure AI, AWS Bedrock / SageMaker or GCP Vertex AI) and APIs. Data engineering skills: SQL, pandas and building data pipelines. Understanding of secure and responsible AI practices. Cybersecurity, GRC or risk domain experience. Experience integrating with ServiceNow, Archer, OneTrust or security tool APIs. MLOps tooling (MLflow, Docker, CI/CD). Knowledge of Saudi AI and data regulations (SDAIA AI Ethics Principles, PDPL). Arabic language is a plus. At least one preferred: Microsoft Azure AI Engineer (AI-102), AWS Certified Machine Learning (Specialty or Engineer), Google Professional Machine Learning Engineer. Also valued: ISO/IEC 42001 Lead Implementer, IAPP AIGP. NIST AI RMF 1.0, ISO/IEC 42001, SDAIA AI Ethics Principles, OWASP Top 10 for LLM Applications, PDPL, NCA ECC-2:2024.

Find and prioritise opportunities; Work with domain leads to map manual, repetitive and high-volume tasks across strategy, technical assessments, risk, compliance, TPRM, governance and performance management; Build a prioritised backlog of AI and automation use cases, with expected time savings, risk and effort for each; Track AI advancements and good practice, and share them with the Innovation Guidance Team and the wider programme; Design, build and deploy solutions such as: Evidence and document analysis (policies, contracts, SOC 2 reports, compliance evidence); TPRM automation (pre-filling and reviewing supplier questionnaires and flagging gaps); Vulnerability analytics (enriching and prioritising findings); Reporting automation (draft assessment reports, dashboards, executive summaries); AI assistants (search frameworks, policies and past work); Anomaly detection (SOC-relevant); Build data pipelines and integrations with security tools, GRC and TPRM platforms, ticketing systems and Power BI; Choose the right approach for each problem (rules, scripts, classical ML or LLMs). Enable the team: Document solutions clearly and train specialists; Measure adoption and time saved, and report results to programme leadership with the Performance Management team.