The role combines solution architecture leadership with deep expertise in GenAI application design, agentic systems, and AI cloud architecture on AWS and Snowflake....AI Cloud & Platform Architecture
• Design and govern the cloud architecture for AI/ML/GenAI platforms across AWS and Snowflake.
• Define scalable patterns for compute, storage, networking, identity, security, environment setup, and enterprise integration across development, test, and production environments.
• Build architecture patterns for MLOps and LLMOps covering deployment, experimentation, model lifecycle management, monitoring, retraining, and version control.
• Ensure platform reliability, resilience, observability, compliance, and cost optimization for AI solutions in production.
• Establish architecture standards and reference frameworks that accelerate AI solution delivery across business domains....Productionization, Engineering & Governance
• Collaborate with Data Engineering, Platform Engineering, DevOps, and Security teams to productionize AI and GenAI solutions in scalable cloud environments.
• Design CI/CD and automation patterns for model deployment, prompt/version management, testing, release control, and operational support.
• Implement monitoring for model drift, prompt drift, performance degradation, usage patterns, system health, and data integrity.
• Support governance, risk management, and responsible AI initiatives by embedding security, privacy, compliance, and auditability into solution design.
• Innovation & Technical Leadership
• Stay current with advancements in foundation models, multimodal AI, agent frameworks, orchestration patterns, and cloud-native AI services.
• Provide architecture guidance, design reviews, and technical mentorship across cross-functional teams.
• Contribute to enterprise-wide AI best practices, reusable frameworks, and...Qualifications & Experiences:
• Bachelor's degree, Master's degree, or higher in Computer Science, Data Science, Engineering, Mathematics, Statistics, or a related quantitative discipline.
• 8+ years of relevant experience in solution architecture, data science, machine learning, or AI engineering, with at least 2+ years of experience in Generative AI / LLM-based solutions.
• Demonstrated track record of designing and delivering production-grade AI/ML/GenAI solutions with measurable business impact.
• Strong experience in architecting enterprise AI solutions across business workflows, data ecosystems, and cloud platforms.
• Hands-on expertise in building and scaling GenAI and LLM applications, including prompt engineering, RAG architectures, semantic search, embeddings, and evaluation frameworks.
• Experience designing or supporting agentic AI systems, including orchestration, tool usage, memory, guardrails, and human oversight patterns.
• ...ough secure data access patterns, vector search, and enterprise data integration.
• Familiarity with embedding models, semantic search, and vector storage technologies, including OpenSearch vectors and Snowflake Cortex Search.
• Understanding of LLM evaluation considerations within platform architecture, including performance monitoring, cost optimization, and quality metrics.
• Experience designing MLOps and LLMOps architectures for model deployment, monitoring, retraining, and lifecycle governance.
• Strong expertise in AWS cloud architecture, including VPC, IAM, S3, ECS/EKS, SageMaker, Bedrock, and observability tooling.
• Hands-on experience with infrastructure as code, CI/CD pipelines, and containerized AI workloads (Docker, Kubernetes).
• Working knowledge of Snowflake architecture, including Snowpark, Snowpipe, Streams & Tasks, and Cortex AI.
• Deep understanding of Responsible AI, data security, privacy controls, and governance requirements for enterprise AI platforms.