Lead Security Engineer - Python

About This Role

Leading a high-performing team to deliver secure software solutions is the core of this role, focusing on preventing misuse, circumvention, and malicious behavior. As a core technical contributor, you will be responsible for implementing critical technology solutions with tamper-proof, audit-defensible methods across multiple technical areas within various business functions.

Responsibilities

• Execute creative security solutions through design, development, and technical troubleshooting, thinking beyond routine or conventional approaches to build solutions and break down complex technical problems
• Use enterprise-authorized AI capabilities within the work environment to accelerate threat modeling, vulnerability analysis synthesis, and security documentation, validating outputs and ensuring sensitive data is handled appropriately
• Develop secure, high-quality production code, and review and debug code written by others
• Minimize security vulnerabilities by applying industry insights and governmental regulations to continuously evolve security protocols; create processes to determine control effectiveness; and conduct discovery, vulnerability assessments, penetration testing, and threat scenario analysis to identify and assess vulnerabilities
• Work with stakeholders and business leaders to understand security needs and recommend business modifications during periods of vulnerability
• Apply reuse-first, AI-assisted practices within SDLC and toolchain routines to strengthen security testing and control validation, ensuring traceability, auditability, and alignment to resiliency and security expectations

Requirements

• Design and build secure Python solutions (APIs, microservices, automation tools, security utilities) using secure-by-design patterns, least privilege, and defense-in-depth
• Perform secure code development, review, and debugging for Python and adjacent stacks (e.g., containerized workloads, CI/CD), including fixing security defects and improving engineering guardrails
• Demonstrate expertise in cloud and DevOps technologies, including AWS, Google Cloud, Azure, Docker, Kubernetes, Jenkins, and GitLab
• Be skilled in working with databases such as MongoDB, PostgreSQL, and NoSQL systems
• Maintain a strong background in data science and AI, including deep learning, NLP, GenAI, RAG concepts, prompt engineering, and data visualization
• Apply experience in secure coding, model validation, and data privacy
• Exhibit excellent problem-solving, communication, and teamwork skills

Nice to have:

• Experience with LLM fine-tuning, prompt engineering, and multi-modal AI integration
• Familiarity with CI/CD pipelines and automation tools
• Knowledge of frontend frameworks such as React and Next.js