AI Platform Technical Architect

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 / 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 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.