Intake IT Solutions
Job Title:
Gen AI Architect Location:
Santa Clara, CA Duration:
Contract Overview
Gen AI Architect responsible for designing and delivering enterprise‑grade Generative AI solutions, including LLM and multi‑agent architectures, across cloud platforms. Lead architecture strategy, technical governance, and collaboration with cross‑functional teams and customers to enable scalable, secure GenAI deployments. Responsibilities
Architect scalable GenAI solutions and LLM/multi‑agent systems with emphasis on scalability, resilience, and security across Azure and Google Cloud Platform. Define technology strategy and provide guidance to customers and internal teams; align GenAI projects with business outcomes. Architect and guide development of LLM‑powered applications, assistants, and RAG pipelines for structured and unstructured data. Define and implement agentic AI architectures leveraging frameworks such as LangGraph, AutoGen, DSPy, and cloud‑native orchestration tools. Oversee integration of OpenAI, Azure OpenAI, and Google Cloud Vertex AI models into enterprise systems, including MuleSoft Apigee connectors. Establish LLMOps practices (CI/CD, monitoring, optimization, cost control) and enforce responsible AI guardrails (bias detection, prompt injection protection, hallucination reduction). Lead architecture reviews, governance boards, and technical design authority for all LLM initiatives. Collaborate with data scientists, engineers, and business teams to translate use cases into scalable, secure solutions. Define and maintain best practices, playbooks, and technical documentation for enterprise adoption. Guide monitoring and observability efforts, including dashboards for usage, adoption, ingestion health, and platform performance. Support secondary activities such as innovation/research, PoCs, ecosystem stewardship, and alignment with business goals. Qualifications
Educational Qualification : Bachelor’s or Master’s degree in Computer Science, Data Science, or a related field. Experience : Total IT experience 15+ years and 10–12 years in AI/ML‑related roles with a strong focus on LLMs and agentic AI technology. Primary (Must have skills)
(to be screened by TA team): Generative AI Solution Architecture (2–3 years): design and architecture of GenAI applications, including Retrieval-Augmented Generation (RAG), LLM orchestration (LangChain, LangGraph), and advanced prompt design. Backend & Integration Expertise (5 years): Python‑based microservices, APIs, and orchestration layers enabling 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 Google Vertex AI with emphasis on scalability, security, and performance. RAG & Data Pipeline Design (2–3 years): data ingestion, embedding generation, vector search using Azure Cognitive Search, Pinecone, Weaviate, FAISS, or Vertex Matching Engine. LLM Optimization & Adaptation (2–3 years): fine‑tuning and parameter‑efficient tuning (LoRA, QLoRA, PEFT) and memory modules to enhance agent intelligence. Multi‑Agent Orchestration (2–3 years): design of multi‑agent frameworks and pipelines (LangChain, AutoGen, DSPy) for goal‑driven planning and tool/invocation.
Performance Engineering (2–3 years): optimize Vertex AI models for latency, throughput, and 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, governance for LLM apps, including content moderation and data protection. Knowledge, Skills & Attributes
Communication Skills : effective communication with internal and customer stakeholders via verbal, email, and messaging. Interpersonal Skills : strong ability to build relationships, provide constructive feedback, and collaborate across teams. Problem‑Solving & Analytical Thinking : analytical mindset and capability to troubleshoot and translate ideas into technology solutions. Work Updates : experience in Agile/Scrum with tools like Jira/Azure DevOps; provide regular, proactive updates. Expected Outcome & Additional Plans
Expected outcomes are defined through engagement with stakeholders and ongoing PoCs. Secondary skills and training plans may be defined post‑hiring as needed.
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Gen AI Architect Location:
Santa Clara, CA Duration:
Contract Overview
Gen AI Architect responsible for designing and delivering enterprise‑grade Generative AI solutions, including LLM and multi‑agent architectures, across cloud platforms. Lead architecture strategy, technical governance, and collaboration with cross‑functional teams and customers to enable scalable, secure GenAI deployments. Responsibilities
Architect scalable GenAI solutions and LLM/multi‑agent systems with emphasis on scalability, resilience, and security across Azure and Google Cloud Platform. Define technology strategy and provide guidance to customers and internal teams; align GenAI projects with business outcomes. Architect and guide development of LLM‑powered applications, assistants, and RAG pipelines for structured and unstructured data. Define and implement agentic AI architectures leveraging frameworks such as LangGraph, AutoGen, DSPy, and cloud‑native orchestration tools. Oversee integration of OpenAI, Azure OpenAI, and Google Cloud Vertex AI models into enterprise systems, including MuleSoft Apigee connectors. Establish LLMOps practices (CI/CD, monitoring, optimization, cost control) and enforce responsible AI guardrails (bias detection, prompt injection protection, hallucination reduction). Lead architecture reviews, governance boards, and technical design authority for all LLM initiatives. Collaborate with data scientists, engineers, and business teams to translate use cases into scalable, secure solutions. Define and maintain best practices, playbooks, and technical documentation for enterprise adoption. Guide monitoring and observability efforts, including dashboards for usage, adoption, ingestion health, and platform performance. Support secondary activities such as innovation/research, PoCs, ecosystem stewardship, and alignment with business goals. Qualifications
Educational Qualification : Bachelor’s or Master’s degree in Computer Science, Data Science, or a related field. Experience : Total IT experience 15+ years and 10–12 years in AI/ML‑related roles with a strong focus on LLMs and agentic AI technology. Primary (Must have skills)
(to be screened by TA team): Generative AI Solution Architecture (2–3 years): design and architecture of GenAI applications, including Retrieval-Augmented Generation (RAG), LLM orchestration (LangChain, LangGraph), and advanced prompt design. Backend & Integration Expertise (5 years): Python‑based microservices, APIs, and orchestration layers enabling 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 Google Vertex AI with emphasis on scalability, security, and performance. RAG & Data Pipeline Design (2–3 years): data ingestion, embedding generation, vector search using Azure Cognitive Search, Pinecone, Weaviate, FAISS, or Vertex Matching Engine. LLM Optimization & Adaptation (2–3 years): fine‑tuning and parameter‑efficient tuning (LoRA, QLoRA, PEFT) and memory modules to enhance agent intelligence. Multi‑Agent Orchestration (2–3 years): design of multi‑agent frameworks and pipelines (LangChain, AutoGen, DSPy) for goal‑driven planning and tool/invocation.
Performance Engineering (2–3 years): optimize Vertex AI models for latency, throughput, and 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, governance for LLM apps, including content moderation and data protection. Knowledge, Skills & Attributes
Communication Skills : effective communication with internal and customer stakeholders via verbal, email, and messaging. Interpersonal Skills : strong ability to build relationships, provide constructive feedback, and collaborate across teams. Problem‑Solving & Analytical Thinking : analytical mindset and capability to troubleshoot and translate ideas into technology solutions. Work Updates : experience in Agile/Scrum with tools like Jira/Azure DevOps; provide regular, proactive updates. Expected Outcome & Additional Plans
Expected outcomes are defined through engagement with stakeholders and ongoing PoCs. Secondary skills and training plans may be defined post‑hiring as needed.
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