AI Solutions Engineer

Bangalore

3-5 Years

About Indusface:

Indusface is a leading application security SaaS company that secures critical Web, Mobile, and API applications of 6500+ global customers using its award-winning fully managed platform that integrates web application scanner, web application firewall, DDoS & BOT Mitigation, CDN, and threat intelligence engine.

Indusface, funded by Tata Capital Growth Fund II, is the only vendor to receive 100% customer recommendation rating three years in a row and is a global customer choice in the Gartner Peer Insights™ Web Application and API Protection (WAAP) Report 2023. Indusface is also a “Great Place to Work” 2022 Winner in the Mid-Size category in India and is PCI, ISO27001, SOC 2, GDPR certified and has been the recipient of many prestigious start-up awards.

Indusface’s Product Suite:

AppTrana WAAP Platform - AI-Powered, Fully Managed Application and API Protection.
Indusface WAS Platform - Fully Managed DAST Platform for Website, Mobile App & API Vulnerability Scanning.
SSL/VMC Certificates - Secure your online digital communication and build customer trust.

Why Join Us?

Be part of a Market Leader - Work with an industry-recognized cybersecurity company trusted by global enterprises.
Shape the Future - Play a pivotal role in driving innovation, partnerships, and revenue growth.
Fast-Paced Growth - Thrive in an agile, high-growth environment with opportunities to make a real impact.
Collaborate with the Best - Work alongside industry experts and thought leaders.
Rewards & Recognition - Competitive salary, performance-based incentives, and clear career growth pathways

Roles & Responsibilities:

Design, develop, and implement robust data pipelines for collecting, cleaning, and preparing data for model training and evaluation, specifically from web and API traffic, and security event logs.
Select appropriate machine learning models, with a particular emphasis on smaller, efficient models suitable for security applications (e.g., WAF, bot detection, anomaly detection, API threat prevention) and other performance-critical use cases.
Train, fine-tune, and evaluate machine learning models, employing techniques to optimize for performance, cost, and accuracy in identifying and mitigating security threats.
Deploy models into production environments, establishing and managing MLOps processes for continuous integration, delivery, and training (CI/CD/CT) within our cloud security infrastructure.
Monitor model performance in production, implementing strategies for regular re-tuning and updates to ensure continued accuracy and relevance against evolving threat landscapes.
Collaborate with product management and engineering teams to understand requirements, define AI solutions, and integrate them into existing products and new features for web and API security.
Drive the evolution of our MLOps practices to enhance the speed, reliability, and scalability of our AI deployments, fostering a culture of continuous improvement and innovation in AI operations.
Stay up-to-date with the latest advancements in applied AI, MLOps, and relevant technologies, particularly in cybersecurity AI, threat intelligence, and Generative AI for security.
Document AI solutions, processes, and model performance for internal stakeholders.

Job Description:

3-5 years of hands-on experience in applied AI or machine learning engineering, preferably in a cybersecurity context.
Proven experience in developing, deploying, and maintaining machine learning models in production environments for security use cases.
Strong proficiency in Python and relevant AI/ML libraries/frameworks (e.g., Scikit-learn, TensorFlow Lite, PyTorch, ONNX, Hugging Face Transformers, MLflow, Kubeflow).
Hands-on experience with data cleaning, feature engineering, model selection, and hyperparameter tuning, particularly for smaller, efficient models tailored to security data.
Demonstrable experience in building and maintaining robust data pipelines and CI/CD/CT for ML systems.
Software development experience in building high-performant, secure, and scalable web applications or security services.
Fair understanding of dynamically scalable cloud architectures, ideally AWS.
Excellent problem-solving and analytical skills.
Strong verbal and written communication skills.
Collaborative, quality-conscious, and self-motivated with a proactive approach.
A passion for building, deploying, and meticulously managing the full lifecycle of impactful AI systems.
Experience with security AI use cases like anomaly detection, threat intelligence, or user behaviour analytics.
Experience with Layer 7 security concepts, web application firewalls (WAF), API security, and bot mitigation techniques.

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