AI Security Engineer

Flipkart · Bengaluru

  • Experience2–6 yrs
  • SalaryNot disclosed
  • Work modeonsite
  • Levelmid
  • Posted2 Sept 2026
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About Flipkart

Flipkart is hiring in Bengaluru in ecommerce retail. This role looks for around 2+ years of experience.

Skills

  • Python
  • TensorFlow
  • PyTorch
  • scikit-learn
  • Hugging Face
  • Transformers
  • FAISS
  • Chroma
  • REST APIs
  • Docker
  • Kubernetes
  • Git
  • CI/CD
  • OWASP Top 10
  • OWASP LLM Top 10
  • IAM

The role

An AI security engineer at an e-commerce marketplace builds machine learning pipelines and secures generative AI applications through prompt guardrails, OWASP LLM Top 10, and cloud security. Python automation and Kubernetes deployment support monitored AI workloads and secure integrations.

Full job description

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).