Technical Architect

Technical Architect (AI / ML) Full Time, San Jose, CA (Onsite) Role Description – As a Technical Architect specializing in LLMs and Agentic AI, you will own the architecture, strategy, and delivery of enterprise-grade AI solutions. 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. Qualifications 10‑16 years of experience in AI/ML‑related roles, with a strong focus on LLMs & Agentic AI technology 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 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, Cosmos DB Vector 2‑3 years of experience in LLM architectures, RAG pipelines & patterns, evaluation frameworks, embeddings, tokenization, prompt engineering, evaluation strategies, hallucination reduction and Agentic AI frameworks, multi‑agent orchestrations and frameworks; 1‑2 years of experience in Agentic AI frameworks, MCP, A2A 2‑3 years of experience building GenAI applications, agent workflows, or knowledge retrieval systems using frameworks like LangChain, LlamaIndex, Graph RAG, or custom implementations Bachelor's or Master's degree in Computer Science, Data Science, or a related field Prior experience in working on Agile/Scrum projects with exposure to tools like Jira / Azure DevOps Strong interpersonal skills to build and maintain productive relationships with team members & customer reps Provide constructive feedback during code reviews and be open to receiving feedback on your own code Analytical mindset; ability to bring idea into reality through technology implementation & adoption Problem‑solving and analytical thinking; capability to troubleshoot and resolve issues efficiently Provides regular updates, proactive and due diligent to carry out responsibilities Communicate effectively with internal and customer stakeholders; communication approach: verbal, emails and instant messages 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 Secondary Skills Automation for data pipelines, feature engineering, model training, validation, packaging, deployment, versioning, and rollback Implementing model observability, drift monitoring, logging, tracing, metrics, experiment tracking, and governance Familiarity with end‑to‑end evaluation workflows for LLMs including latency, throughput, cost optimization, caching, and fallback strategies Experience with containerization, Kubernetes, Istio / Linked, service mesh patterns Familiarity with feature stores, knowledge graphs, ontology and metadata platform AI benchmarking, evaluation frameworks (RAGAS, Promptfoo, LangSmith, TruLens) Experience working in Agile, product‑based delivery #J-18808-Ljbffr