Senior AI Solutions Architect

SeeClear; India; On-site; INR 4,000,000 - 7,500,000; Full time

Job summary

SeeClear is seeking a hands-on Enterprise Architect to own the solution and technical architecture end to end for an AI-led enterprise application in India. You will own the web app, its surrounding services, and the AI tier that bridges user questions to system answers.

You will lead full-stack delivery on the .NET stack with React/Angular, RESTful services, and cloud deployment with Azure, Docker and Kubernetes. Strong focus on LLM capabilities, tool usage, and security reviews.

Qualifications

• Experience leading architecture engagements for full-stack web applications.
• Ability to design RESTful and event-driven services with backward compatibility.
• Production experience with AI/LLM applications and tool calling.

Responsibilities

• Own the solution and technical architecture end to end for the AI-led enterprise app.
• Lead architecture engagements with multiple engineering teams and developers.
• Design and implement modern front-end (React/Angular) and backend (.NET/Java/Node) stacks.
• Prototype and govern data structures, patterns, and security controls for enterprise use.

Skills

Full-stack architecture

Azure cloud

Docker & Kubernetes

LLM applications

Security & governance

Team leadership

Tools

ASP.NET Core

Web API

C#

React

Angular

LangChain

Semantic Kernel

LangGraph

AutoGen

Azure DevOps

Job description

This is a hands-on architecture role on an AI-led enterprise application. You will own the solution and technical architecture end to end - the web application, the services around it, and the AI tier that sits between a user's question and the systems that can answer it.

• Has led architecture engagements in full-stack web application development, not only advised on them.
• Has worked as an architect alongside tech leads, multiple engineering teams and multiple developers.
• Full-stack delivery on the .NET stack - ASP.NET Core, Web API, C#, with .NET Framework or MVC legacy exposure useful for modernisation work. A strong Java (Spring Boot) or Node/TypeScript background with .NET willingness will be considered.
• Modern front end: React or Angular with TypeScript, component architecture, and streaming or incremental UI for long-running AI responses.
• RESTful and event-driven service design, API-led connectivity, versioning and backward compatibility across consumers.
• Data structures, design patterns, and the judgement to know when a pattern is costing more than it returns.
• High-level and low-level design as written artifacts - sequence, component and deployment views, and a non-functional section that survives a security review.
• Production experience with LLM applications: prompt and context design, structured output, function and tool calling, streaming, and failure handling when the model returns something unusable.
• Agentic patterns - planner/executor, tool-using agents, multi-agent hand-off, human-in-the-loop checkpoints - and a view on where each is warranted.
• Model Context Protocol: building and operating MCP servers, designing tool and resource schemas, and passing caller identity through to the underlying system rather than running everything as a service account.
• Custom orchestration logic in code. Familiarity with Semantic Kernel, LangGraph, LangChain or AutoGen is useful, but we expect you to know what the framework is doing and to be comfortable replacing it where it does not fit.
• Retrieval design: chunking and indexing strategy, hybrid vector and keyword search, reranking, citation, and entitlement filtering applied at retrieval time, not after.
• Guardrails and safety controls appropriate to an enterprise portal: PII handling, prompt injection resistance for tool-using agents, output validation, and full audit logging.
• Evaluation as engineering, not sampling by eye - golden question sets, regression gates in CI, tracing and observability, and cost and latency measured per interaction.
• Enterprise authentication and authorisation with Active Directory or Microsoft Entra ID - SSO, OIDC and SAML, on-behalf-of flows, service principals, and managed identities.
• Group-based entitlement design carried through the full path: portal, services, agents, tools and the semantic model, including row-level and object-level security.
• Audit, traceability and data-residency considerations for AI features, and the ability to hold that conversation with a customer's security team without escalating everything.
• Built and deployed applications on a public or private cloud - Azure preferred given the AD and data platform context, with AWS or GCP acceptable.
• Docker and Kubernetes in anger: workload sizing, secrets, configuration, scaling behaviour for GPU or token-bound workloads, and what to do when a pod restarts mid-conversation.
• CI/CD with Azure DevOps or GitHub Actions, infrastructure as code, and prompt and model versioning treated with the same discipline as application code.
• An active role in development, code review, continuous integration and deployment. This is not a diagram-only role.
• Has worked directly with customer and business stakeholders, business analysts and project managers to shape and deliver business use cases.
• Comfortable with abstract problem statements in ambiguous customer environments, and able to work independently and give technical leadership to the team.
• Can explain an architectural trade-off to a non-technical sponsor and a security reviewer in the same meeting, and take a position rather than list options.
• Strong first-principles thinking. Depth over breadth of tool names.
• Strong written and spoken English; strong analytical, co-ordination and organisational skills.
• Enterprise application integration and distributed systems architecture - microservices, event-based integration, ESB or iPaaS exposure.
• Knowledge graphs, ontologies or metadata catalogues supporting AI retrieval.
• Legacy modernisation and pre-sales or discovery experience - sizing, phasing and naming uncertainty in front of a customer.
• Contributions to internal capability building: reusable accelerators, reference architectures or practice development around AI and integration.