Full Stack Architect Agentic AI

SRS Consulting Inc
San Francisco, United States of America
yesterday

Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Working hours
Regular working hours
Languages
English
Experience level
Senior

Job location

San Francisco, United States of America

Tech stack

Agile Methodologies
Artificial Intelligence
Amazon Web Services (AWS)
Azure
Google BigQuery
Cloud Computing
Cloud Engineering
Cloud Storage
Continuous Integration
DevOps
Django
Github
Python
Software Engineering
Google Cloud Platform
Cloud Monitoring
Spring Cloud
React
Flask
Large Language Models
Multi-Agent Systems
Prompt Engineering
Multi-Cloud
Generative AI
Firebase
Build Server
Backend
FastAPI
Vue.js
Angular
Gitlab-ci
Kubernetes
Google Cloud Functions
GraphQL
Front End Software Development
Virtual Agents
Api Gateway
REST
Docker
Jenkins
Microservices

Job description

We are seeking a highly skilled and client-facing Full Stack Architect with strong expertise in Python backend development, modern front-end frameworks, and Google Cloud Platform (Google Cloud Platform) to lead the design of next-gen Agentic AI solutions. In this role, you will architect intelligent, cloud-native applications that leverage LLMs, autonomous agents, and Google Cloud Platform-native services to deliver scalable AI capabilities for high-tech customers., * Architect and deliver end-to-end AI-powered solutions for high-tech clients using Google Cloud Platform-native components and Agentic AI frameworks.

  • Design scalable backend services using Python (FastAPI, Flask, or Django) and integrate them with LLMs and autonomous agent frameworks (LangChain, AutoGen, CrewAI).
  • Define solution architectures leveraging Google Cloud Platform services such as Vertex AI, BigQuery, Cloud Functions, Pub/Sub, Cloud Run, and Firestore.
  • Lead the design and implementation of frontend applications using React, Angular, or Vue, ensuring seamless UX/UI integration with AI capabilities.
  • Collaborate with clients, product managers, and engineering teams to capture business requirements and convert them into technical roadmaps.
  • Drive technical workshops, POCs, and architectural reviews focused on AI/ML and cloud transformation strategies.
  • Implement and optimize vector database integrations (e.g., Pinecone, Weaviate, FAISS) and embedding pipelines on Google Cloud Platform.
  • Define and enforce best practices in cloud-native DevOps, microservices, and CI/CD automation using Google Cloud Platform tools like Cloud Build, Artifact Registry, and Cloud Monitoring.
  • Provide architectural guidance and mentorship to distributed engineering teams following Agile delivery models.

Requirements

  • 10+ years of experience in full stack architecture and software engineering, ideally in high-tech product or platform environments.
  • Strong hands-on experience with Python backend frameworks (FastAPI, Flask, Django).
  • Proficient in frontend development using Angular with a solid understanding of UX patterns.
  • Hands-on experience with Agentic AI frameworks such as LangChain, AutoGen, or CrewAI.
  • Deep knowledge of LLM APIs (OpenAI, Claude, Gemini, Mistral) and prompt engineering strategies.
  • Solid experience with Google Cloud Platform services including Vertex AI, BigQuery, Pub/Sub, Cloud Storage, Cloud Functions, and Cloud Run.
  • Familiarity with vector databases and retrieval-augmented generation (RAG) pipelines.
  • Expertise in REST, GraphQL, microservices architecture, and API gateways.
  • Proficient in Docker, Kubernetes (GKE preferred), and CI/CD pipelines using Cloud Build or equivalent.
  • Strong communication skills and ability to engage with both technical and business stakeholders.
  • Experience working with Agile methodologies and distributed delivery teams.

Preferred skills:

  • Google Cloud Platform Certification (e.g., Professional Cloud Architect, Professional Data Engineer) is a strong plus.
  • Experience in CI/CD implementation on DevOps platforms (e.g., GitLab CI/CD, Cloud Build, Jenkins, or GitHub Actions).
  • Familiarity with multi-cloud deployments (AWS, Azure) in addition to Google Cloud Platform.

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