Chief Architect

• Platform Architecture Design: Own end-to-end AI/ML platform architecture across data ingestion, processing, ML training, inference, MLOps, and GenAI layers
• Reference Architecture Development: Define comprehensive reference architectures for datasets, models, feature stores, model registries, metadata management, orchestration, and AI services
• Technology Selection: Evaluate and recommend technologies, frameworks, and tools
• GenAI & Advanced AI: Guide architectural decisions for GenAI, LLM fine-tuning, RAG (Retrieval Augmented Generation), vector databases, intelligent search, and conversational AI
• MLOps & Model Lifecycle: Design CI/CD pipelines for ML models, A/B testing frameworks, model monitoring, drift detection, and automated retraining mechanisms
• Security & Compliance: Ensure alignment with security, compliance, data sovereignty, and regulatory requirements
• Data Architecture: Design data lake, lakehouse, and data mesh architectures
• Integration & APIs: Design API strategies, microservices architecture, event-driven patterns, and integration with enterprise systems
• Performance & Scalability: Architect for high availability (99.9%+), low latency
• Technical Leadership: Provide architectural oversight and technical assurance throughout delivery
• Innovation & Best Practices: Stay current with emerging AI/ML technologies, research papers, and industry trends

• Bachelor's/Master's degree in Computer Science, AI/ML, or related field
• PhD preferred
• Cloud architecture certifications (AWS Solutions Architect Professional, Azure Solutions Architect Expert, GCP Professional Cloud Architect)
• 15+ years in software/platform architecture with 8+ years specializing in AI/ML systems
• Proven experience architecting 3+ large-scale AI/ML platforms serving 1000+ users
• Published papers, patents, or open-source contributions in AI/ML domain preferred
• AI/ML and data platform architecture with hands-on experience in TensorFlow, PyTorch, scikit-learn, Hugging Face
• MLOps and enterprise model lifecycle management using MLflow, Kubeflow, SageMaker, or Azure ML
• GenAI and intelligent search architectures including LLMs (GPT, LLaMA, Mistral), RAG, vector databases (Pinecone, Weaviate, Milvus)
• Cloud platforms (AWS, Azure, GCP) and Kubernetes orchestration at enterprise scale
• Data engineering tools (Spark, Airflow, Kafka, Databricks, Snowflake)
• Security, compliance, and data governance frameworks (RBAC, encryption, audit logging)
• Large-scale enterprise platform design with microservices, event-driven architecture, and API management
• Open-source technologies and contribution to ML/AI communities

Published papers, patents, or open-source contributions in AI/ML domain preferred