Senior Full Stack Product Developer

This is a fully remote position, open to applicants in India.

📋 Description

• Design and develop security products featuring self-service portals (UI), security central websites utilizing Angular JS, React JS, Backstage, and backend APIs using Java or Python.

• Create CI-CD pipelines on Tekton and deploy them to the GCP cloud.

• Collaborate with product managers, subject matter experts, and key stakeholders to build prototypes and conduct proof of concepts (PoCs).

• Work alongside platform vendors to comprehend their APIs and integration patterns for the automation of deployments and manual tasks.

• Automate testing processes using tools like Playwright and Postman.

• Partner with various infrastructure and application support teams to offer technical guidance and address security concerns.

• Design, test, and implement security policies for critical enterprise infrastructure.

• Automate repetitive tasks and workflows to enhance process efficiency through the development of APIs/scripts and their deployment to the cloud.

• Implement Site Reliability Engineering (SRE) for platform services, capabilities, and features to ensure availability and reliability.

• Safeguard in-house and public AI, ML/DL/LLM/Agents/MCP systems against cyber threats, adversarial attacks, and data breaches throughout the solution lifecycle.

• Design and establish robust security platforms that meet enterprise security requirements, such as a unified telemetry pipeline like BindPlane, SIEM solutions like QRadar, SecOps, and AI security.

• Define and uphold guidelines and controls for securing AI systems, addressing data protection, model security, and compliance necessities.

• Utilize established frameworks as reference points or baselines, including the Google Secure AI Framework (SAIF), NIST AI Risk Management Framework, and the Framework for AI Cybersecurity Practices (FAICP).

• Identify, evaluate, and mitigate AI-specific security risks, including adversarial attacks, data poisoning, model inversion, and unauthorized access.

• Conduct vulnerability assessments and penetration testing on AI models and data pipelines.

• Ensure that AI data is encrypted, anonymized, and securely stored.

• Implement access controls for sensitive AI data and models, utilizing RBAC, ABAC, and Zero Trust principles.

• Protect AI models from tampering, theft, and adversarial manipulation during both training and deployment phases.

• Monitor and log activity within AI systems to detect anomalies and security incidents.

• Develop and enforce policies that align AI systems with industry regulations, ethical standards, and organizational governance requirements.

• Create automated workflows and scripts to enhance the functionality and scalability of security platforms, thereby improving operational efficiency.

• Manage timely patching and upgrades of security tools and systems to minimize downtime and vulnerabilities.

• Configure alerting systems for potential security threats and enable real-time monitoring for observability.

⛳️ Requirements

• A relevant Bachelor's or Master’s Degree in engineering or computer applications.

• Over 4 years of experience in product development, focusing on UI (Angular JS, React, Backstage) and API development (Python Flask, FastAPI, or Java).

• More than 4 years of experience in security engineering, platform engineering, and AI/ML, particularly in large, complex environments, including:

• Management of security platforms and tools in enterprise settings.

• Experience with telemetry pipeline platforms (e.g., BindPlane), SIEM tools (e.g., Splunk, QRadar), and vulnerability management solutions.

• At least 4 years of experience in developing CI-CD pipelines.

• Over 4 years of experience with GCP, Azure, or AWS cloud services, and configuring infrastructure using infrastructure-as-code libraries such as Terraform and Ansible.

• Knowledge of container security (Docker, Kubernetes).

• Familiarity with networking protocols, firewalls, and best practices in network security.

• Understanding of AI/ML concepts, architectures, and associated security challenges.

• Awareness of AI threat areas, including adversarial attacks, data poisoning, model inversion, and unauthorized access.

• Experience in conducting vulnerability assessments and penetration testing on AI models and data pipelines.

• Proficiency in data protection techniques such as encryption, anonymization, and secure storage, along with secure access management (RBAC, ABAC, Zero Trust).

• Knowledge of incident response, monitoring tools, and threat intelligence platforms.

• Familiarity with security frameworks and compliance references such as SAIF, NIST, and FAICP.

• Experience working in an Agile development environment.

• A solid understanding of cybersecurity principles, practices, and technologies.

🏝️ Benefits

• NOTE: Apply using the External Facing Resume