S&P Global•4h ago
Foundit
GenAI Cloud Security Engineer
Hyderabad / Secunderabad, Telangana, India
Full Time
Senior Level
N/A
N/A
N/A
Responsibilities
Qualifications & Requirements
Experience Level: Senior Level
Full Job Description
GenAI Cloud Security Engineer - S&P Global Hyderabad
About the Role: S&P Global's Cloud Security team is seeking a talented GenAI Cloud Security Engineer to join our dynamic group. This role is pivotal in safeguarding our organization's digital assets, data, and applications within cloud environments. Our primary focus is on ensuring the integrity, confidentiality, and resilience of AI models, specifically targeting the detection and mitigation of vulnerabilities, the implementation of robust encryption, and the protection against potential misuse of generated outputs. The team operates globally, with members located in New York, New Jersey, London, Delhi NCR, and Hyderabad.
Key Responsibilities
- Develop and implement comprehensive AI/ML security strategies, policies, standards, and guidelines to protect organizational assets and ensure the secure operation of AI and ML systems.
- Build security control frameworks and generic reference architectures for GenAI applications.
- Assist LoB/Dev teams in identifying essential security requirements for building GenAI applications.
- Conduct threat modeling exercises to identify potential security risks and vulnerabilities in AI systems, integrating security into design and development processes with AI development teams.
- Provide thought leadership and innovative solutions to mature Gen AI security governance, embedding it into our existing cybersecurity risk appetite framework.
- Perform security assessments on AI applications and systems to identify and address vulnerabilities. Develop and implement testing methodologies to evaluate the security posture of AI models and frameworks.
- Develop configuration hardening guidelines for Cloud Services, including native Generative AI/ML services such as AWS SageMaker, SageMaker Notebooks, Bedrock, Kendra, OpenSearch, Lambda, Azure Cognitive Services, OpenAI, and GCP Vertex AI.
- Stay current with relevant regulations and standards related to AI security and ensure compliance. Collaborate with legal and compliance teams to align AI systems with industry and regulatory requirements.
Core Skills Required
- Strong programming experience in Python (preferred) or equivalent languages.
- Solid understanding of LLM / GenAI fundamentals: prompting, embeddings, vector search, RAG, and basic agentic patterns (tool use, planning, orchestration).
- Experience running production systems or data pipelines on AWS / Azure / GCP, utilizing containers, serverless, and managed storage/services.
- Hands-on familiarity with observability tools (OpenTelemetry, Prometheus, Grafana, ELK, etc.) across logs, metrics, and traces.
- Comfort working with structured and unstructured data; strong SQL proficiency plus experience with Pandas / Spark / dbt or similar frameworks.
- Ability to reason clearly about reliability, performance, and cost trade-offs.
- Strong collaboration and communication skills; ability to translate complex concepts for platform, product, data, security, and compliance teams.
Qualifications
- 15 years of experience in cybersecurity, software engineering, data engineering, ML engineering, or data science.
- Bachelor's or Master's degree in Computer Science, Engineering, Data Science, or equivalent practical experience.
- Experience with CI/CD, code reviews, and modern engineering best practices.
- Nice to Have:
- Exposure to agentic AI frameworks (LangChain, LangGraph, OpenAI Agents, etc.).
- Experience with LLM observability, eval frameworks, or prior work on production LLM/agent systems.
What We're Looking For
We seek engineers who demonstrate the following attributes:- Build for scale: Think like platform builders and design systems that serve broader needs, not just immediate use cases.
- Lead with observability: Prioritize instrumentation, leverage data for debugging, and deliver insightful dashboards.
- Ship safely: Implement guardrails and validations before deployment, even if it requires additional upfront effort.
- Make thoughtful trade-offs: Clearly articulate decisions regarding cost, quality, latency, and reliability.
- Own the end-to-end stack: Demonstrate proficiency across data pipelines, agent logic, infrastructure, and production monitoring.
- Learn through experimentation: Embrace testing ideas, analyzing failures, iterating rapidly, and pursuing continuous improvement.
- Communicate with impact: Effectively explain complex AI concepts in simple, business-relevant terms to both technical and non-technical stakeholders.
- Stay ahead of the curve: Actively explore emerging technologies such as LangGraph, agentic frameworks, and new LLM capabilities.
Company
S&P Global
Hyderabad / Secunderabad, Telangana, India
Posted on Foundit