We're Hiring: AI Solution Architect / Principal Architect

Join us in building next-generation AI-native, web, and mobile applications. We're looking for experienced architects with strong expertise in cloud-native architectures, DevOps practices, and scalable enterprise solutions.

Architecture Scope:

- Intelligent (AI-Native) Applications, Web Applications, and Mobile Applications

Cloud (Mandatory):

- Cloud-native architecture experience on Google Cloud Platform (GCP), AWS, or Azure

DevOps (Mandatory):

- Hands-on experience with CI/CD, Containers, Infrastructure Automation, and Production Deployments

Reporting To: Practice Lead

Role Summary:

We are looking for an Architect who designs and delivers AI-native applications for enterprise clients. You will own end-to-end architecture across intelligent apps, web and mobile, lead solutioning for new opportunities, and present to senior client stakeholders. You stay close to the code and guide engineering teams through to production delivery.

Key Responsibilities:

- Define end-to-end architecture for AI-native applications: agentic workflows, conversational interfaces, RAG pipelines, integrations and data flows.

- Design agentic AI systems using LangChain, LangGraph or similar frameworks, covering orchestration, tool use, memory, multi-agent patterns and human-in-the-loop controls.

- Architect solutions that span web and mobile channels, with a working understanding of mobile architecture (native iOS/Android, React Native or Flutter) and mobile-to-backend integration.

- Lead solutioning for pre-sales and new engagements: discovery, solution design, effort estimation, architecture decks and proposal inputs.

- Present architecture and solution recommendations to CXO, business and technical stakeholders; run workshops and design reviews.

- Manage stakeholder relationships across client, delivery and product teams; align on scope, trade-offs and priorities.

- Own technical delivery: guide fullstack leads and engineers, review designs and code, remove blockers, and ensure quality releases.

- Set architecture standards for scalability, security, performance, cost, responsible AI, LLM evaluation and observability.

- Design cloud-native deployments on GCP, AWS or Azure, including managed AI services (Vertex AI, Bedrock, Azure OpenAI), containers, serverless and CI/CD.

- Track emerging AI technologies and turn them into reusable accelerators and reference architectures.

Required Skills and Experience:

- 10+ years of software engineering experience, including several years in solution or application architecture roles.

- Proven architecture experience on intelligent/AI-powered applications; at least an understanding of mobile application architecture.

- Minimum 1 year of hands-on agentic AI application development (LangChain, LangGraph or equivalent).

- Strong fullstack foundation: React plus Node.js and/or Python back ends, API design, microservices and event-driven systems.

- Deep knowledge of LLMs, prompt engineering, embeddings, vector databases, RAG and agent evaluation.

- Must have cloud-native architecture experience on at least one of Google Cloud, AWS or Azure.

- Mandatory hands-on DevOps experience, including CI/CD architecture, container orchestration, infrastructure as code, release automation, observability and production operations.

- Excellent solutioning skills: able to turn business problems into clear architectures, estimates and roadmaps.

- Excellent communication and presentation skills, comfortable with senior and non-technical audiences.

- Strong stakeholder management experience in client-facing or consulting environments.

- Track record of technical delivery of complex, multi-team programs to production.

Nice to Have:

- Expertise in one or more business domains (e.g., BFSI, healthcare, retail, manufacturing, logistics).

- Experience with Model Context Protocol (MCP), CrewAI, AutoGen or Semantic Kernel.

- Architecture certifications (AWS Solutions Architect, Azure Solutions Architect Expert, Google Professional Cloud Architect, TOGAF).

- Experience with LLMOps/MLOps tooling and AI governance frameworks.