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Senior Solution Architect
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Senior Solution Architect

Senior Solution Architect at AI tech startup · Remote only · 5 - 10 years · ₹15L - ₹23L / yr · Remote only · Posted 7 Jan 2026

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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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Technical leadership

  • A proven track record overseeing product architecture, tech strategy and cross-functional engineering execution across core full-stack and AI platforms.
  • Collaborate with executive leadership on business strategy, client requirements and product delivery.
  • Build, mentor and scale high-performing engineering teams with a growth mindset.
  • Establish a strong technology culture grounded in ownership, innovation and continuous learning.

What success looks like

  • Mission AI: build a world-class AI system leveraging GenAI, ML and enterprise-grade data engineering.
  • Hyper-scaling: architect and scale the platform to match global industry leaders in the IP space.
  • Culture building: develop a strong engineering organisation with high ownership, performance and innovation DNA.

Qualifications & experience

  • Preferably under 15 years of enterprise software engineering experience across B2B SaaS or technology-first companies; fewer is fine for an exceptional engineer.
  • A proven track record of taking early-stage AI/ML prototypes and scaling them into robust enterprise SaaS platforms featuring multi-agent orchestration, complex workflows and decision intelligence.
  • Experienced in building and mentoring agile, lean startup teams of full-stack and machine learning engineers from the ground up.
  • Proven leadership in defining and executing technology strategy and platform roadmaps.
  • Extensive cloud-native engineering experience with Azure, AWS and GCP.

Technical expertise

  • Strong full-stack engineering background (Java, Python, JavaScript frameworks).
  • Expertise with JS frameworks such as React, Angular and Node.js.
  • Experience building and scaling distributed systems and microservices.
  • Strong knowledge of databases (SQL, NoSQL), data modelling and unstructured data management.
  • Strong understanding of Agile methodologies and tools (Atlassian, Git, CI/CD pipelines).

Behavioural & leadership competencies

  • Product and delivery management expertise, end to end, including delivery and customer support.
  • Excellent communication, with the ability to influence executive stakeholders.
  • High technical proficiency combined with strong business acumen.
  • Strong analytical and decision-making skills.


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aarushi Mahajan
Posted by aarushi Mahajan
UAE
7 - 13 yrs
₹20L - ₹32L / yr
skill iconAmazon Web Services (AWS)
Azure OpenAI
Google Cloud Platform (GCP)
CI/CD
DevOps
+9 more

Job Description:

  • Infrastructure Management: Design, implement, and manage scalable, reliable, and secure cloud infrastructure using AWS, GCP, and/or Azure.
  • CI/CD Pipelines: Develop and maintain continuous integration and continuous deployment (CI/CD) pipelines to streamline the development lifecycle.
  • Automation: Automate infrastructure provisioning, configuration management, and application deployment processes.
  • Monitoring and Performance: Implement monitoring, logging, and alerting solutions to ensure system health, performance, and reliability.
  • Security: Ensure the security of cloud infrastructure and applications, including identity management and compliance with industry standards.
  • Collaboration: Work closely with client and development teams to integrate DevOps practices and deliver high-quality software.
  • Documentation: Maintain comprehensive documentation of infrastructure, configurations, and processes.
  • Innovation: Stay current with emerging technologies and industry trends, integrating them into the DevOps strategy as appropriate.


Qualifications

  • Education: Bachelor's degree in Computer Science, Information Technology, or a related field.
  • Experience: 7 - 10 years of overall experience with relevant experience of at least 7 years in DevOps and served as a lead or senior engineer.
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Mohammed Rabidheen
Posted by Mohammed Rabidheen
Coimbatore
3 - 8 yrs
Best in industry
Windows Azure
AKS
DevOps
Microsoft Windows Azure

Senior Cloud Site Reliability Engineer (CSRE) – Azure


About Searce:

Searce is an AI-native, engineering-led modern technology consultancy that empowers

clients to futurify their businesses by delivering real, intelligent business outcomes. As a

trusted partner for over 3,000 clients globally, Searce specializes in cloud modernization,

data engineering, applied AI, and robust cloud platform security. Driven by a "HAPPIER"

cultural mindset and our proprietary evlos problem-solving framework, we eliminate

bureaucratic fluff to build working prototypes fast and scale enterprise production

environments intelligently. We don't just fix systems; we leverage multi-cloud technologies

to transform client operations into distinct competitive advantages.

Position Overview:

We are looking for a high-caliber Senior or Lead Cloud Site Reliability Engineer (CSRE) to

architect, secure, and stabilize next-generation hybrid and multi-cloud environments.

Operating at the intersection of infrastructure design, security compliance, and production

operations, you will serve as the technical Subject Matter Expert (SME) across GCP, Azure,

and AWS.

Whether optimizing a microservice mesh on GKE, tuning autoscaling on AKS, or driving a

massive disaster recovery drill across AWS regions, your focus will be absolute reliability. For

the Lead path, you will couple this deep engineering toolkit with stakeholder management

and mentorship to drive an elite operational culture.


Experience & Level Expectation:

Years of Experience: 3 to 10 years of intensive, hands-on production operations

experience in a dedicated DevOps, Cloud Platform Engineering, or SRE role.

Associate level (3-5 Years): Expected to show flawless execution of IaC, advanced

triaging of infrastructure failures, and ownership of the CI/CD and deployment

lifecycles.

Intermediate level (5-10 Years): Expected to take architectural ownership, serve as

primary Incident Commander for complex outages, design cross-cloud governance

frameworks, and act as a reliable bridge between technical teams and client

leadership.


Key Responsibilities & Role Expectations:

Multi-Cloud Platforms & Orchestration: Design, configure, and maintain

production-grade Kubernetes clusters across major platforms (AKS).

Manage advanced network routing, service meshes (e.g., Istio), and multi-tenant

isolation.

Infrastructure as Code (IaC) & GitOps: Build declarative, enterprise-grade, reusable

infrastructure components using Terraform or Crossplane. Standardize automated

environment provisioning to eliminate configuration drift across multi-branch

environments.

Incident Management & Reliability (SRE): Own and optimize the production on-call

rotation. Lead rapid mitigation strategies for Sev-1/Sev-2 system outages, reducing

Mean Time to Recovery (MTTR) through centralized log and metric correlation.

Root Cause Analysis (RCA): Facilitate rigorous, blameless post-incident reviews to

identify core architectural vulnerabilities and establish long-term fixes preventing

recurrence.

Lifecycle, Patching & Upgrades: Plan and execute zero-downtime cluster upgrades,

operating system patching strategies (Linux/Windows), database lifecycle updates,

and multi-region Disaster Recovery (DR) failover drills.

Core Core Operations & Legacy Integration: Manage enterprise-level hybrid

networking architecture (VPCs, Firewalls, Load Balancers, DNS routing, and DHCP

configurations) while effectively connecting cloud native services to legacy

infrastructures like Active Directory.

Security & Governance: Embed Zero Trust policies, secure secrets management

(Secrets Manager/Key Vault), and continuous vulnerability patching into the

automated SDLC pipeline.


Required Technical Skills:

- Microsoft Azure: Azure Virtual Machines, Virtual Networks, Azure Active Directory, Azure Update Management.

- Containers & Orchestration

  • Production-level management of GKE, AKS, and EKS.
  • Advanced mastery of Docker, Helm, Kubernetes StatefulSets, Pod Disruption
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