Job Title: AI Platform Technical Architect
Location: San Jose CA
Duration: Long term contract
Job Description:
- 6-10 years of experience in Designing and implementing large-scale distributed systems microservices serverless and event-driven architectures.
- 5-8 years of experience in Cloud-native architecture experience in Azure / AWS / GCP 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 computer 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 Lang Chain Prompt Flow 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 Cosmos DB 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 Lang Chain Llama Index Graph RAG or custom implementations.
Technical skills:
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 Responsibilities:
- Architect scalable and secure AI/ML/LLM platform solutions including data model and inference pipelines.
- Establish enterprise reference architectures reusable components best practices and governance standards for AI adoption.
- Integrate cloud-native open-source and enterprise tools such as vector databases feature stores registries and orchestration frameworks.
- Implement automated MLOps/LLMOps workflows covering deployment monitoring observability compliance and performance optimization.
- Collaborate with cross-functional teams (engineering data science security and product) to align platform capabilities with business goals and drive adoption.
Secondary Responsibilities:
- Support GenAI and AI application teams by providing platform enablement solution advisory and architecture reviews.
- Conduct technology research PoCs benchmarking and evaluate emerging AI tools frameworks and deployment patterns.
- Drive knowledge sharing through documentation workshops training sessions and internal community building initiatives.
- Provide guidance on cost estimation usage monitoring finops optimization and capacity planning.
- Partner with security compliance and cloud teams to ensure alignment with regulatory data privacy and policy frameworks.
Job Title: AI Platform Technical Architect Location: San Jose CA Duration: Long term contract Job Description: 6-10 years of experience in Designing and implementing large-scale distributed systems microservices serverless and event-driven architectures. 5-8 years of experience in Cloud-na...
Job Title: AI Platform Technical Architect
Location: San Jose CA
Duration: Long term contract
Job Description:
- 6-10 years of experience in Designing and implementing large-scale distributed systems microservices serverless and event-driven architectures.
- 5-8 years of experience in Cloud-native architecture experience in Azure / AWS / GCP 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 computer 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 Lang Chain Prompt Flow 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 Cosmos DB 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 Lang Chain Llama Index Graph RAG or custom implementations.
Technical skills:
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 Responsibilities:
- Architect scalable and secure AI/ML/LLM platform solutions including data model and inference pipelines.
- Establish enterprise reference architectures reusable components best practices and governance standards for AI adoption.
- Integrate cloud-native open-source and enterprise tools such as vector databases feature stores registries and orchestration frameworks.
- Implement automated MLOps/LLMOps workflows covering deployment monitoring observability compliance and performance optimization.
- Collaborate with cross-functional teams (engineering data science security and product) to align platform capabilities with business goals and drive adoption.
Secondary Responsibilities:
- Support GenAI and AI application teams by providing platform enablement solution advisory and architecture reviews.
- Conduct technology research PoCs benchmarking and evaluate emerging AI tools frameworks and deployment patterns.
- Drive knowledge sharing through documentation workshops training sessions and internal community building initiatives.
- Provide guidance on cost estimation usage monitoring finops optimization and capacity planning.
- Partner with security compliance and cloud teams to ensure alignment with regulatory data privacy and policy frameworks.
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