Job Title: Technical Architect – AI
Location: San Francisco, CA - Onsite
Only Local Candidates
As a Technical Architect specializing in LLMs and Agentic AI, you will own the architecture, strategy, and delivery of enterprise-grade AI solutions. You will work with cross-functional teams and customers to define the AI roadmap, design scalable solutions, and ensure responsible deployment of Generative AI across the organization
Primary Skills :
6-10 years of experience in Designing and implementing large-scale distributed systems, microservices, serverless, and event-driven architectures.
5-8 years of experience n Cloud-native architecture experience in Azure / AWS / Google Cloud Platform including networking, storage, compute scaling, GPU workloads, and managed AI services.
5-8 years of experience with platform components, API design, integration patterns, and high-performance compute architecture.
4-7 years of experience building or integrating AI/ML platforms, pipelines, model lifecycle components, inference gateways, and/or enterprise GenAI frameworks.
3-6 years of experience using AI platform tools such as Databricks, Vertex AI, Azure AI Studio, AWS Bedrock, LangChain, PromptFlow, Ray, Kubeflow, MLflow, Airflow, Kafka, etc.
2-5 years of experience in designing and integrating vector database solutions such as Pinecone, Weaviate, FAISS, Milvus, Qdrant, Elastic, OpenSearch, CosmosDB Vector.
2-3 years of experience in LLM architectures, embeddings, tokenization, prompt engineering, evaluation strategies, hallucination reduction, and RAG patterns.
2-3 years of experience building GenAI applications, agent workflows, or knowledge retrieval systems using frameworks like LangChain, LlamaIndex, GraphRAG, or custom implementations.
Secondary Skills:
6-10 years of experience in Designing and implementing large-scale distributed systems, microservices, serverless, and event-driven architectures.
5-8 years of experience n Cloud-native architecture experience in Azure / AWS / Google Cloud Platform including networking, storage, compute scaling, GPU workloads, and managed AI services.
5-8 years of experience with platform components, API design, integration patterns, and high-performance compute architecture.
4-7 years of experience building or integrating AI/ML platforms, pipelines, model lifecycle components, inference gateways, and/or enterprise GenAI frameworks.
3-6 years of experience using AI platform tools such as Databricks, Vertex AI, Azure AI Studio, AWS Bedrock, LangChain, PromptFlow, Ray, Kubeflow, MLflow, Airflow, Kafka, etc.
2-5 years of experience in designing and integrating vector database solutions such as Pinecone, Weaviate, FAISS, Milvus, Qdrant, Elastic, OpenSearch, CosmosDB Vector.
2-3 years of experience in LLM architectures, embeddings, tokenization, prompt engineering, evaluation strategies, hallucination reduction, and RAG patterns.
2-3 years of experience building GenAI applications, agent workflows, or knowledge retrieval systems using frameworks like LangChain, LlamaIndex, GraphRAG, or custom implementations
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