AI Architect

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Position : AI 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 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 / 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 .
• 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 Apigee)
• Data & Vector DBs (BigQuery ChromaDB Vector Search)

Thanks and Regards

Sushil Kaushik

MITS LLC