AI Architect - Google AI & Generative Intelligence

I Architect - Google AI & Generative Intelligence

Experience Required: 12-18 Years in Software Engineering | 7+ Years in AI/ML & Generative AI

Employment Type: Full-Time

Location: Paramus, NJ / Hybrid



Role Overview
We are seeking a highly accomplished I Architect with deep expertise in Google AI technologies and Generative AI to lead the design and implementation of enterprise-scale AI solutions. This role requires strong architectural vision, hands-on technical depth, and leadership in building production-grade AI systems leveraging LLMs, SLMs, and multi-agent frameworks .
The ideal candidate will drive AI strategy, define scalable architectures, and lead cross-functional teams in delivering cutting-edge AI-powered applications using the Google Cloud ecosystem , modern AI frameworks, and robust MLOps practices.



Key Responsibilities

1. AI Architecture & Strategy

• Define end-to-end I/GenAI architecture for enterprise-grade applications.
• Establish best practices for LLM/SLM adoption, multi-agent systems, and RAG architectures .
• Drive AI platform strategy leveraging Google Cloud (Vertex AI, GKE, Cloud Run) .
• Lead architecture reviews, technical governance, and design standards.

2. LLM / SLM & Generative AI Solutions

• rchitect solutions using commercial LLMs such as Gemini, GPT, and Claude .
• Design scalable systems using open-source models (Mixtral, Mistral, Gemma, Phi-3) .
• Define strategies for fine-tuning (LoRA, QLoRA, PEFT) and model optimization.
• Oversee model evaluation frameworks and benchmarking (HELM, lm-eval, RAGAS).

3. Google AI Ecosystem Leadership

• Lead adoption of:
  • Vertex AI for model lifecycle management
  • Google Agent Development Kit (ADK) for intelligent agents
  • Google Workspace integrations (Docs, Sheets, Gmail, Drive, Meet)
• rchitect solutions using BigQuery, Lakehouse, and Vector Databases .

4. AI Platform & MLOps Architecture

• Design scalable MLOps pipelines for training, deployment, and monitoring.
• Define CI/CD strategies for AI systems using GitHub Actions / GitLab CI .
• Establish observability frameworks using LangSmith, MLflow, Weights & Biases .
• Optimize infrastructure cost and performance across cloud and hybrid environments.

5. Multi-Agent Systems & AI Frameworks

• rchitect complex workflows using:
  • LangChain, LlamaIndex, LangGraph
  • Semantic Kernel for multi-agent orchestration
• Design intelligent automation pipelines and agent collaboration patterns.

6. Data & RAG Architecture

• Design enterprise RAG pipelines using Vertex AI Vector DB, ChromaDB .
• Define data ingestion, transformation, and governance strategies.
• rchitect semantic search and knowledge retrieval systems.

7. Application & Integration Architecture

• Define backend architecture using FastAPI / Node.js APIs .
• rchitect API management and security using pigee / MuleSoft .
• Guide frontend architecture using React / Angular for AI-driven applications.

8. Engineering Leadership

• Provide technical leadership and mentorship to AI/ML engineers.
• Collaborate with product, data, and engineering teams for solution delivery.
• Lead design documentation, architecture diagrams, and technical roadmaps.
• Ensure adherence to coding standards, testing, and quality frameworks.

9. Deployment & Infrastructure

• rchitect deployments across:
  • GCP (Vertex AI, GKE, Cloud Run)
  • Hybrid and on-prem environments
  • Edge AI use cases
• Ensure scalability, reliability, and security of AI systems.

10. AI Governance & Responsible AI

• Define frameworks for I ethics, bias mitigation, and explainability .
• Establish governance for model lifecycle, monitoring, and compliance .
• Implement safeguards for hallucination detection and output validation.

Required Qualifications

• 12-18 years of software engineering experience.
• 7+ years in AI/ML with strong focus on Generative AI and LLMs .
• Deep expertise in Google AI ecosystem (Vertex AI, Gemini, ADK, AI Studio) .
• Strong experience in LLMs, SLMs, RAG, and multi-agent architectures .
• Proficiency in Python and familiarity with Node.js .
• Hands-on experience with MLOps, CI/CD, and cloud-native architecture (GCP) .
• Proven experience designing scalable, production-grade AI systems .

Preferred Qualifications

• Google Cloud Certifications ( Professional ML Engineer / Cloud Architect ).
• Experience contributing to open-source AI/ML projects .
• Expertise in edge AI and hybrid cloud deployments .
• Experience building enterprise AI platforms or COEs .
• Strong leadership experience mentoring and scaling AI teams.

Key Skills Summary

• Generative AI (LLMs, SLMs, RAG, Agents)
• Google Cloud AI Stack (Vertex AI, Gemini, ADK)
• I Frameworks (LangChain, LangGraph, LlamaIndex, Semantic Kernel)
• MLOps & Observability (MLflow, W&B, LangSmith)
• Cloud & Infrastructure (GCP, Kubernetes, Serverless)
• Backend & APIs (FastAPI, Node.js, Apigee)
• Data & Vector DBs (BigQuery, ChromaDB, Vector Search)