Solution Architect

Overview

We are looking for an AI Solution Architect to design and build intelligent AI applications on top of Context Intelligence product. The role combines hands-on AI engineering with strong knowledge of enterprise application architecture, data models and business workflows, enabling AI applications to retrieve context, provide recommendations and execute authorized actions across enterprise systems.

Responsibilities

Architect and build AI copilots, assistants and agentic applications using contextual knowledge from the Context Substrate and live enterprise data. Translate business requirements into end-to-end AI workflows spanning CRM, service management, ERP and other enterprise applications. Design integrations to retrieve information, update records and execute business workflows through enterprise APIs and tools.

Define context retrieval, prompt construction, agent orchestration, tool calling and human approval mechanisms. Develop reusable connectors and API services; address data mapping, synchronization, API limits, failures and transaction consistency. Ensure permission-aware access, secure credential handling, auditability, application reliability and observability.

Lead discovery, architecture reviews, proofs of value and production delivery; guide engineering teams and communicate with stakeholders.

• Evaluate retrieval relevance, response accuracy, action correctness, latency, cost and business outcomes.

Requirements

10+ years in software engineering, enterprise integration or solution architecture, with demonstrated hands-on delivery of AI/GenAI applications.

• Strong architecture and integration expertise in at least two enterprise platforms, preferably Salesforce and Service. Now. Understand their data models, workflows, security models, extension mechanisms and API capabilities.
• Ability to analyze enterprise application dependencies and design AI workflows across systems of record.
• Hands-on expertise in Python, backend/API development, LLMs, RAG, agent orchestration and tool calling.
• Experience consuming context services, vector databases and knowledge graphs to ground AI responses and decisions.
• Strong knowledge of REST/SOAP APIs, webhooks, event-driven integration, middleware, OAuth 2.0 and role-based access controls.
• Experience deploying scalable applications on Azure, AWS or GCP, using containers, CI/CD and monitoring.
• Strong problem-solving, customer communication and technical leadership skills. Architect and build AI copilots, assistants and agentic applications using contextual knowledge from the Context Substrate and live enterprise data. Translate business requirements into end-to-end AI workflows spanning CRM, service management, ERP and other enterprise applications. Design integrations to retrieve information, update records and execute business workflows through enterprise APIs and tools. Define context retrieval, prompt construction, agent orchestration, tool calling and human approval mechanisms. Develop reusable connectors and API services; address data mapping, synchronization, API limits, failures and transaction consistency. Ensure permission-aware access, secure credential handling, auditability, application reliability and observability. Lead discovery, architecture reviews, proofs of value and production delivery; guide engineering teams and communicate with stakeholders.
• Evaluate retrieval relevance, response accuracy, action correctness, latency, cost and business outcomes. 10+ years in software engineering, enterprise integration or solution architecture, with demonstrated hands-on delivery of AI/GenAI applications.
• Strong architecture and integration expertise in at least two enterprise platforms, preferably Salesforce and Service. Now. Understand their data models, workflows, security models, extension mechanisms and API capabilities.
• Ability to analyze enterprise application dependencies and design AI workflows across systems of record.
• Hands-on expertise in Python, backend/API development, LLMs, RAG, agent orchestration and tool calling.
• Experience consuming context services, vector databases and knowledge graphs to ground AI responses and decisions.
• Strong knowledge of REST/SOAP APIs, webhooks, event-driven integration, middleware, OAuth 2.0 and role-based access controls.
• Experience deploying scalable applications on Azure, AWS or GCP, using containers, CI/CD and monitoring.
• Strong problem-solving, customer communication and technical leadership skills.