Senior Solution Architect

AI tech startup

Senior Solution Architect

Recruit Square

Company

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Senior Solution Architect

at AI tech startup

Agency job

via Recruit Square

5 - 10 yrs

₹15L - ₹23L / yr

Remote only

Skills

SaaS

Artificial Intelligence (AI)

Solution architecture

Product development

Large Language Models (LLM)

Docker

Kubernetes

Fullstack Developer

Key Responsibilities

1. Platform & Application Architecture

Architect and oversee end-to-end platform design using GCP services (Vertex AI, Cloud Run, GKE, Pub/Sub, Firestore, BigQuery, and IAM).
Define the architecture for multi-tenant SaaS deployment—ensuring secure data boundaries across customers while maintaining a single codebase.
Collaborate closely with backend developers to optimize Node.js APIs, data pipelines, and event-driven microservices.
Design front-end integration flows and ensure UI/UX alignment with backend AI and agentic processes.

2. AI / LLM / Agentic System Architecture

Lead architecture for all AI initiatives involving RAG pipelines, AI agents, NLP, and conversational AI.
Evaluate and integrate LLMs and APIs from OpenAI, Google Gemini, Anthropic (Claude), Mistral, and others.
Define frameworks for AI agent orchestration, prompt engineering, and function/tool calling workflows using frameworks like LangChain or LlamaIndex.
Establish standards for vector databases (Pinecone, FAISS, Chroma), embeddings, chunking strategies, and retrieval optimization.
Architect an AgentOps layer for monitoring, safety filtering, and AI model governance.

3. MLOps, PromptOps & DevOps Integration

Implement CI/CD pipelines for AI models, prompts, and agents using Cloud Build, Terraform, and GitHub Actions.
Design PromptOps and MLOps pipelines to manage model lifecycle, versioning, rollback, and production promotion.
Define observability and monitoring mechanisms (OpenTelemetry, Cloud Monitoring, custom logs for LLM outputs).
Collaborate with DevOps engineers to define blue/green and canary deployments across environments.

4. Security, Compliance & Scalability

Design tenant-isolated data layers using IAM, VPC-SC, and KMS encryption policies to meet regulatory standards (HIPAA, GxP, GDPR).
Implement data governance, access control, and threat detection mechanisms for AI-driven workflows.
Create performance optimization strategies: GPU/TPU allocation, autoscaling policies, token budgets, response caching, and FinOps visibility.

5. Cross-Functional Leadership

Partner with Product Managers and Business Analysts to translate business requirements into architectural blueprints.
Work with UX/UI designers to ensure front-end experiences align with AI agent capabilities and backend APIs.
Provide architectural guidance and technical mentorship to developers, ensuring adherence to best practices across projects.
Contribute to platform evolution, reference architecture libraries, and reusable design patterns.

Key Skills & Qualifications

Technical Expertise

Strong background in cloud architecture, specifically Google Cloud Platform (GCP)—Vertex AI, Cloud Run, Cloud Storage, IAM, Pub/Sub, BigQuery, VPC, and Cloud Functions.
Proficiency with Node.js, TypeScript, and API development for scalable microservices.
Deep understanding of LLMs, AI agent frameworks, retrieval-augmented generation (RAG), and vector search.
Experience with LangChain, LlamaIndex, CrewAI, and Agentic AI design patterns.
Strong understanding of data architecture, including NoSQL/SQL databases (MongoDB, Firestore, PostgreSQL).
Hands-on experience with container orchestration (Docker, Kubernetes) and Infrastructure-as-Code (Terraform).

Preferred Skills

Prior experience designing multi-tenant SaaS platforms.
Exposure to frontend integration (Angular, React, or similar) and UI/UX flow understanding.
Knowledge of AI safety, risk frameworks, and prompt governance.
Strong analytical and communication skills for working across business and technical stakeholders.
Awareness of FinOps and cost optimization in token-based AI ecosystems.

Qualifications

Bachelor’s or Master’s degree in Computer Science, AI, or Cloud Architecture.
8–12 years of professional experience, with at least 3 years focused on AI platform architecture or enterprise-grade SaaS system design.

Certifications in GCP Professional Cloud Architect or Machine Learning Engineer are a plus.

Key Responsibilities

1. Platform & Application Architecture

Architect and oversee end-to-end platform design using GCP services (Vertex AI, Cloud Run, GKE, Pub/Sub, Firestore, BigQuery, and IAM).
Define the architecture for multi-tenant SaaS deployment—ensuring secure data boundaries across customers while maintaining a single codebase.
Collaborate closely with backend developers to optimize Node.js APIs, data pipelines, and event-driven microservices.
Design front-end integration flows and ensure UI/UX alignment with backend AI and agentic processes.

2. AI / LLM / Agentic System Architecture

Lead architecture for all AI initiatives involving RAG pipelines, AI agents, NLP, and conversational AI.
Evaluate and integrate LLMs and APIs from OpenAI, Google Gemini, Anthropic (Claude), Mistral, and others.
Define frameworks for AI agent orchestration, prompt engineering, and function/tool calling workflows using frameworks like LangChain or LlamaIndex.
Establish standards for vector databases (Pinecone, FAISS, Chroma), embeddings, chunking strategies, and retrieval optimization.
Architect an AgentOps layer for monitoring, safety filtering, and AI model governance.

3. MLOps, PromptOps & DevOps Integration

Implement CI/CD pipelines for AI models, prompts, and agents using Cloud Build, Terraform, and GitHub Actions.
Design PromptOps and MLOps pipelines to manage model lifecycle, versioning, rollback, and production promotion.
Define observability and monitoring mechanisms (OpenTelemetry, Cloud Monitoring, custom logs for LLM outputs).
Collaborate with DevOps engineers to define blue/green and canary deployments across environments.

4. Security, Compliance & Scalability

Design tenant-isolated data layers using IAM, VPC-SC, and KMS encryption policies to meet regulatory standards (HIPAA, GxP, GDPR).
Implement data governance, access control, and threat detection mechanisms for AI-driven workflows.
Create performance optimization strategies: GPU/TPU allocation, autoscaling policies, token budgets, response caching, and FinOps visibility.

5. Cross-Functional Leadership

Partner with Product Managers and Business Analysts to translate business requirements into architectural blueprints.
Work with UX/UI designers to ensure front-end experiences align with AI agent capabilities and backend APIs.
Provide architectural guidance and technical mentorship to developers, ensuring adherence to best practices across projects.
Contribute to platform evolution, reference architecture libraries, and reusable design patterns.

Key Skills & Qualifications

Technical Expertise

Strong background in cloud architecture, specifically Google Cloud Platform (GCP)—Vertex AI, Cloud Run, Cloud Storage, IAM, Pub/Sub, BigQuery, VPC, and Cloud Functions.
Proficiency with Node.js, TypeScript, and API development for scalable microservices.
Deep understanding of LLMs, AI agent frameworks, retrieval-augmented generation (RAG), and vector search.
Experience with LangChain, LlamaIndex, CrewAI, and Agentic AI design patterns.
Strong understanding of data architecture, including NoSQL/SQL databases (MongoDB, Firestore, PostgreSQL).
Hands-on experience with container orchestration (Docker, Kubernetes) and Infrastructure-as-Code (Terraform).

Preferred Skills

Prior experience designing multi-tenant SaaS platforms.
Exposure to frontend integration (Angular, React, or similar) and UI/UX flow understanding.
Knowledge of AI safety, risk frameworks, and prompt governance.
Strong analytical and communication skills for working across business and technical stakeholders.
Awareness of FinOps and cost optimization in token-based AI ecosystems.

Qualifications

Bachelor’s or Master’s degree in Computer Science, AI, or Cloud Architecture.
8–12 years of professional experience, with at least 3 years focused on AI platform architecture or enterprise-grade SaaS system design.

Certifications in GCP Professional Cloud Architect or Machine Learning Engineer are a plus.

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