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Senior Solution Architect
AI tech startup
Senior Solution Architect
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Senior Solution Architect

at AI tech startup

Agency job
5 - 10 yrs
₹15L - ₹23L / yr
Remote only
Skills
SaaS
Artificial Intelligence (AI)
Solution architecture
Product development
Large Language Models (LLM)
skill iconDocker
skill iconKubernetes
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.


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Shubham Vishwakarma

Full Stack Developer - Averlon
I had an amazing experience. It was a delight getting interviewed via Cutshort. The entire end to end process was amazing. I would like to mention Reshika, she was just amazing wrt guiding me through the process. Thank you team.
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