Jobs via Dice
Overview
Technical Architect specializing in LLMs and Agentic AI. Responsible for the architecture, strategy, and delivery of enterprise‑grade AI solutions, working with cross‑functional teams and customers to define the AI roadmap, design scalable solutions, and ensure responsible deployment of Generative AI across the organization. Responsibilities
Architect Scalable GenAI Solutions: Lead the design of enterprise architectures for LLM and multi‑agent systems, ensuring scalability, resilience, and security across Azure and Google Cloud Platform. Technology Strategy & Guidance: Provide strategic technical leadership to customers and internal teams, aligning GenAI projects with business outcomes. LLM & RAG Applications: Architect and guide development of LLM‑powered applications, assistants, and RAG pipelines for structured and unstructured data. Agentic AI Frameworks: Define and implement agentic AI architectures leveraging frameworks like LangGraph, AutoGen, DSPy, and cloud‑native orchestration tools. Integration & APIs: Oversee integration of OpenAI, Azure OpenAI, and Google Cloud Platform Vertex AI models into enterprise systems, including MuleSoft Apigee connectors. LLMOps & Governance: Establish LLMOps practices (CI/CD, monitoring, optimization, cost control) and enforce responsible AI guardrails (bias detection, prompt injection protection, hallucination reduction). Enterprise Governance: Lead architecture reviews, governance boards, and technical design authority for all LLM initiatives. Collaboration: Partner with data scientists, engineers, and business teams to translate use cases into scalable, secure solutions. Documentation & Standards: Define and maintain best practices, playbooks, and technical documentation for enterprise adoption. Monitoring & Observability: Guide implementation of AgentOps dashboards for usage, adoption, ingestion health, and platform performance visibility. Secondary Responsibilities: Innovation & Research in AI platforms, Proof of Concepts, ecosystem expertise across Azure AI and Google Vertex AI, business alignment, and mentorship. Soft Skills: Communicate effectively with internal and customer stakeholders; strong interpersonal skills; problem solving and analytical thinking; proactive updates on Agile/Scrum projects. Qualifications
Educational Qualification: Bachelor's or Master's degree in Computer Science, Data Science, or a related field. Experience Range: Total IT 15+ years; 10–12 years in AI/ML‑related roles with a strong focus on LLMs and agentic AI technology. Primary (Must have skills): Generative AI Solution Architecture (2–3 years): design and architecture of GenAI apps including Retrieval-Augmented Generation, LLM orchestration (LangChain, LangGraph), and advanced prompt design. Backend & Integration: 5+ years in Python microservices, APIs, and orchestration for tool invocation, context management, and task decomposition in cloud‑native environments (Azure Functions, GCP Cloud Functions, Kubernetes). Enterprise LLM Architecture (2–3 years): end‑to‑end LLM solutions using Azure OpenAI, Azure AI Studio, Hugging Face models, and GCP Vertex AI; focus on scalability, security, performance. RAG & Data Pipeline Design (2–3 years): enterprise data ingestion, embedding generation, and vector search using Azure Cognitive Search, Pinecone, Weaviate, FAISS, or Vertex AI Matching Engine. LLM Optimization & Adaptation (2–3 years): fine‑tuning and parameter‑efficient tuning (LoRA, QLoRA, PEFT) and memory modules for enhanced agent intelligence. Multi‑Agent Orchestration (2–3 years): experience with LangChain, AutoGen, or DSPy for goal‑driven planning, task decomposition, and tool/API invocation. Performance Engineering (2–3 years): optimize Vertex AI models for latency, throughput, and scalability in enterprise deployments. AI Application Integration (2–3 years): integrate OpenAI and third‑party models into enterprise apps via APIs and connectors (MuleSoft, Apigee, Azure APIM). Governance & Guardrails (1–2 years): security, compliance, and governance for LLM‑based apps including content moderation, data protection, and guardrails. Other Skills: Version control (Git); Agile methodologies; strong communication and collaboration abilities. Location & Compensation
San Francisco Bay Area. Compensation ranges shown in original posting: various entries such as $90,000.00–$110,000.00, $75,000.00–$95,000.00, $85,000.00–$110,000.00, etc.
#J-18808-Ljbffr
Technical Architect specializing in LLMs and Agentic AI. Responsible for the architecture, strategy, and delivery of enterprise‑grade AI solutions, working with cross‑functional teams and customers to define the AI roadmap, design scalable solutions, and ensure responsible deployment of Generative AI across the organization. Responsibilities
Architect Scalable GenAI Solutions: Lead the design of enterprise architectures for LLM and multi‑agent systems, ensuring scalability, resilience, and security across Azure and Google Cloud Platform. Technology Strategy & Guidance: Provide strategic technical leadership to customers and internal teams, aligning GenAI projects with business outcomes. LLM & RAG Applications: Architect and guide development of LLM‑powered applications, assistants, and RAG pipelines for structured and unstructured data. Agentic AI Frameworks: Define and implement agentic AI architectures leveraging frameworks like LangGraph, AutoGen, DSPy, and cloud‑native orchestration tools. Integration & APIs: Oversee integration of OpenAI, Azure OpenAI, and Google Cloud Platform Vertex AI models into enterprise systems, including MuleSoft Apigee connectors. LLMOps & Governance: Establish LLMOps practices (CI/CD, monitoring, optimization, cost control) and enforce responsible AI guardrails (bias detection, prompt injection protection, hallucination reduction). Enterprise Governance: Lead architecture reviews, governance boards, and technical design authority for all LLM initiatives. Collaboration: Partner with data scientists, engineers, and business teams to translate use cases into scalable, secure solutions. Documentation & Standards: Define and maintain best practices, playbooks, and technical documentation for enterprise adoption. Monitoring & Observability: Guide implementation of AgentOps dashboards for usage, adoption, ingestion health, and platform performance visibility. Secondary Responsibilities: Innovation & Research in AI platforms, Proof of Concepts, ecosystem expertise across Azure AI and Google Vertex AI, business alignment, and mentorship. Soft Skills: Communicate effectively with internal and customer stakeholders; strong interpersonal skills; problem solving and analytical thinking; proactive updates on Agile/Scrum projects. Qualifications
Educational Qualification: Bachelor's or Master's degree in Computer Science, Data Science, or a related field. Experience Range: Total IT 15+ years; 10–12 years in AI/ML‑related roles with a strong focus on LLMs and agentic AI technology. Primary (Must have skills): Generative AI Solution Architecture (2–3 years): design and architecture of GenAI apps including Retrieval-Augmented Generation, LLM orchestration (LangChain, LangGraph), and advanced prompt design. Backend & Integration: 5+ years in Python microservices, APIs, and orchestration for tool invocation, context management, and task decomposition in cloud‑native environments (Azure Functions, GCP Cloud Functions, Kubernetes). Enterprise LLM Architecture (2–3 years): end‑to‑end LLM solutions using Azure OpenAI, Azure AI Studio, Hugging Face models, and GCP Vertex AI; focus on scalability, security, performance. RAG & Data Pipeline Design (2–3 years): enterprise data ingestion, embedding generation, and vector search using Azure Cognitive Search, Pinecone, Weaviate, FAISS, or Vertex AI Matching Engine. LLM Optimization & Adaptation (2–3 years): fine‑tuning and parameter‑efficient tuning (LoRA, QLoRA, PEFT) and memory modules for enhanced agent intelligence. Multi‑Agent Orchestration (2–3 years): experience with LangChain, AutoGen, or DSPy for goal‑driven planning, task decomposition, and tool/API invocation. Performance Engineering (2–3 years): optimize Vertex AI models for latency, throughput, and scalability in enterprise deployments. AI Application Integration (2–3 years): integrate OpenAI and third‑party models into enterprise apps via APIs and connectors (MuleSoft, Apigee, Azure APIM). Governance & Guardrails (1–2 years): security, compliance, and governance for LLM‑based apps including content moderation, data protection, and guardrails. Other Skills: Version control (Git); Agile methodologies; strong communication and collaboration abilities. Location & Compensation
San Francisco Bay Area. Compensation ranges shown in original posting: various entries such as $90,000.00–$110,000.00, $75,000.00–$95,000.00, $85,000.00–$110,000.00, etc.
#J-18808-Ljbffr