AI Security Engineer

Responsibilities:
Build and support AI/ML pipelines, including data preprocessing, training workflows, fine-tuning, and inference optimization.
Develop automation workflows, API integrations, agentic components, and deployment pipelines using Python, Docker/K8s, and CI/CD.
Deploy and monitor AI workloads on cloud platforms.
Implement strong security controls by aligning with OWASP Top 10 OWASP LLM Top 10 and internal AppSec requirements; identify and mitigate risks.
The core requirements for the job include the following:
AI and ML Engineering:
Basic foundational understanding of core ML concepts, including supervised and unsupervised learning, clustering techniques, basic feature engineering, and model evaluation fundamentals.
Knowledge of TensorFlow, PyTorch, scikit-learn, Hugging Face, and Transformers.
Familiarity with vector databases (FAISS, Chroma) and RAG pipelines.
Basic familiarity with Agentic workflows (Autogen, crew, langchain), multi-agent patterns, and MCP (Model Context Protocol) concepts is preferred.
Development and Automation:
Good Python skills for ML pipelines and automation.
Working knowledge of REST APIs, data processing scripts, and backend integration.
Working knowledge of Docker, K8s, Git, and CI/CD pipelines.
AI Security:
Understanding of AI-specific risks: prompt injection, data leakage, jailbreak attempts, insecure RAG, insecure embeddings.
Experience in LLM red-teaming and application of prompt guardrails.
Good understanding of OWASP Top 10 and OWASP LLM Top 10 (AI/LLM-specific).
Foundation in security, IAM, and secure secret handling.
Ops and Platform Skills:
Familiarity with deploying and monitoring AI workloads.
Familiarity with cloud platforms (GCP/AWS/Azure).

Experience
2-6 yrs