AI Architect

Role Name: AI Architect

Work site: Paramus, NJ / Hybrid

Employment Type: Full-Time

Job Summary

AI Architect – Google AI & Generative Intelligence

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

Role Overview

We are seeking a highly accomplished AI 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 AI/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

• Architect 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)
• Architect 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

• Architect 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.
• Architect semantic search and knowledge retrieval systems.

7. Application & Integration Architecture

• Define backend architecture using FastAPI / Node.js APIs.
• Architect API management and security using Apigee / 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

• Architect 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 AI 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)
• AI 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)