AI Security Architect - Manager

We are seeking a highly experiencedPrincipal AI/ML Architect with 12+ years of experience to lead the design and development of enterprise-scale AI platforms, includingagentic AI systems and generative AI solutions.

This role requires deep expertise inLLM architecture, AI product development, and advanced analytics, combined with strong engineering foundations incloud, MLOps, and distributed systems. You will drive end-to-end AI strategy—from research and prototyping to production deployment—while ensuring responsible and governed AI adoption.

Key Responsibilities

AI & GenAI Architecture

• Design and implement scalable AI platforms supportingagentic AI systems and autonomous workflows
• Architect and optimizeLLM-based systems, including fine-tuning, inference, and orchestration
• Build advancedRAG (Retrieval-Augmented Generation) pipelines and multi-agent systems
• Developmultimodal AI systems (text, image, audio) for enterprise use cases
• Lead AI product development from concept to production deployment

Machine Learning & Advanced Analytics

• Develop and deploypredictive models and advanced analytics solutions
• Design scalable ML systems for real-time and batch processing
• Implement optimization techniques such asPEFT, LoRA, QLoRA, and mixed precision training
• Apply reinforcement learning approaches includingRLHF (Reinforcement Learning with Human Feedback)
• Ensure robustness using frameworks likeRAG Triad (retrieval, augmentation, generation evaluation)

Architecture & Engineering

• Design systems usingmicroservices and hexagonal architecture patterns
• Build and maintain scalabledata pipelines and API integrations
• Ensure seamless integration between AI services and enterprise platforms
• Lead system design reviews and ensure high availability, scalability, and security

MLOps & Platform Engineering

• Implement end-to-endMLOps pipelines, including CI/CD for ML systems
• Deploy and manage models usingKubernetes and Docker
• Establishmodel monitoring, drift detection, and performance tracking
• Automate model lifecycle management and continuous retraining workflows

Cloud & Data Platforms

• Architect and manage AI workloads across cloud platforms:
• • AWS (S3, SageMaker, Redshift, Glue)
  • GCP and Azure ecosystems
• Work with modern data platforms such asDatabricks and Cosmos DB
• Optimize large-scale data processing and storage for AI workloads

Responsible AI & Governance

• Define and implementAI governance frameworks
• Ensure compliance with responsible AI principles (fairness, explainability, transparency)
• Implementguardrails and safety mechanisms for LLM systems
• Align AI systems with enterprise risk, compliance, and regulatory requirements