Technical Architect

Technical
Architect (AI / ML)
Full Time
San Jose, CA (Onsite)
Skills / Experience

• 10-16 years of experience in AI/ML-related roles, with a strong focus on LLM's & 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

Role / Job 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.

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