Docker
KubernetesKey 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.
Shubham Vishwakarma
Full Stack Developer - Averlon
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About Us
Spotnana is a stealth startup that is building the technology stack to solve the experience, trust, and transparency challenges that legacy players in a $1.8 trillion industry refuse to. We were told it can’t be done - and now we’re doing it.
Based in the San Francisco Bay Area, our founding team consists of veterans from Google, Amazon, Twitter, Airbnb, Expedia, ThoughtSpot, FreshDesk, Goldman Sachs, Oyo, SAP, and Cohesity
Join us to find out what Silicon Valley investors and early backers of Airbnb, Amazon, Facebook, LinkedIn, Brex, Palantir, Revolut, and Snowflake see in our mission.
Let's explore creating one of the best pieces of work ever made, together.
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Full Stack Developer - Averlon