At YNV Group, we are building an AI-enabled engineering organisation and are looking for a hands-on Solution / Technical Architect who can translate business needs into secure, practical, and deliverable technology solutions. You will work from business context and requirements through architecture, build, and operational handover, leading the design of internal tools, AI agents, workflow automations, and integrated applications.

This is not a role that stops at producing diagrams. You will shape the solution, make the key technical decisions, define the work for engineering teams, guide delivery and verify that the implemented product meets the intended business outcome. You will also design the Microsoft Azure resources required by each solution and ensure that AI-assisted engineering is applied safely through appropriate guardrails, quality controls and end-to-end engineering practices.

The ideal candidate combines business analysis, solution architecture and technical delivery leadership. They can move comfortably between stakeholder conversations, architecture design, Azure configuration, engineering backlog refinement and hands-on validation of working solutions.

Helping people thrive and grow in the modern digital world.

YNV Group has been building successful businesses that deliver long-term value since 2010. As a privately owned holding company, we excel at identifying and addressing unmet market needs. With a global workforce of over 6,000 employees across the Americas, EMEA, and Asia, our portfolio spans technology, real estate, and financial services.

Led by industry experts committed to sustainable growth and innovation, we prioritize building lasting relationships with our clients and partners, ensuring their needs remain central to our approach. The brands in our portfolio include TeKnowledge, Everty, Sandglass, Monifai and Smart Factoring.

Mindset & Ways of Working

- Business-first: starts with the problem, users, process, and measurable outcome before selecting technology
- Hands-on by default: prototypes, validates assumptions, reviews implementation and uses the Azure portal when needed
- Delivery-minded: produces designs that engineers can build, operate, secure, and support
- Clear and decisive: makes trade-offs visible, records decisions and gives development teams actionable direction
- AI-enabled but accountable: uses AI coding tools to accelerate delivery while maintaining human review, traceability and engineering quality
- Secure and responsible: treats identity, data protection, privacy, safety, compliance and misuse prevention as core design concerns
- Pragmatic and adaptable: balances target architecture, delivery pace, cost and technical debt
- Collaborative: works effectively with product owners, business SMEs, developers, data/AI specialists, security, operations and vendors

Key Responsibilities

- Own end-to-end solution architecture from business discovery and requirements clarification through design, build guidance, testing, release, and operational handover
- Understand the business context, user journeys, operating processes, pain points, constraints, and expected benefits, and translate them into clear solution requirements
- Lead the design of tools, AI agents, workflow automations, integrations, and supporting cloud services
- Decompose the target solution into components, interfaces, data flows, user stories, technical enablers, and delivery work packages
- Provide clear technical direction to development teams, including architecture patterns, acceptance criteria, non-functional requirements and implementation guardrails
- Review code, infrastructure, integrations, prompts, agent workflows and technical outputs to ensure alignment with the approved design
- Facilitate design workshops and make proportionate decisions across build, buy, configure and integrate options
- Create and maintain architecture artefacts, including context diagrams, solution designs, data flows, integration contracts, threat models, design decisions and support models
- Identify risks, dependencies, technical debt and delivery constraints early, and drive appropriate resolution or escalation
- Partner with product and delivery leads to shape estimates, sequencing, milestones and release plans
- Ensure solutions are production-ready, observable, supportable, cost-conscious and aligned with enterprise architecture standards
- Contribute reusable reference architectures, templates, patterns and engineering standards to the architecture practice

Solution Design & Delivery Leadership





- Convert functional and non-functional requirements into an implementable architecture covering application, data, integration, security, cloud and operations
- Design modern solutions using APIs, events, microservices, serverless components, low-code/workflow platforms and appropriate packaged services
- Define system boundaries, component responsibilities, interfaces, error handling, resilience, performance, scalability and service-level requirements
- Lead technical discovery and proof-of-concept activity to validate feasibility, value, user experience and key architectural assumptions
- Guide engineers during sprint planning, backlog refinement and delivery, resolving design questions and preventing divergence from the intended architecture
- Define engineering quality gates covering code review, automated testing, security scanning, dependency management, deployment controls and architecture conformance
- Plan the transition into production, including environments, release approach, rollback, monitoring, documentation, support ownership and knowledge transfer
- Assess vendor or platform designs and ensure external delivery teams meet organisational architecture, security and engineering expectations

AI, Agents & Automation Architecture

- Design AI-enabled applications and agentic workflows that combine models, tools, enterprise data, APIs and human decision points
- Determine when to use deterministic automation, generative AI, retrieval-augmented generation (RAG), agentic patterns or a hybrid approach
- Define AI guardrails including authorised tools and actions, prompt and policy controls, content safety, data boundaries, grounding, confidence thresholds, human approval and exception handling
- Design for traceability and evaluation through prompt/version management, model and tool logging, quality measures, test datasets, feedback loops and audit evidence
- Address AI-specific risks such as hallucination, prompt injection, data leakage, excessive agency, unsafe actions, model drift and inappropriate reliance on model output
- Establish secure patterns for model access, secrets, identities, vector stores, knowledge sources, memory, tool invocation and agent-to-system integration
- Apply responsible AI principles and ensure solutions have appropriate transparency, explainability, privacy, accessibility and human oversight
- Define operating controls for AI solutions, including monitoring, usage and cost controls, incident response, change governance and periodic re-evaluation

AI-Enabled Engineering

- Use AI-assisted engineering tools such as GitHub Copilot, Claude Code, Cursor or equivalent to accelerate analysis, design, coding, testing, documentation and troubleshooting
- Define safe usage patterns for AI coding tools, including approved repositories and models, data handling, intellectual property protection, secure prompt practices and prohibited uses
- Ensure AI-generated code is treated as untrusted until reviewed, tested, scanned and validated against engineering standards
- Integrate AI assistance into the software development lifecycle without weakening peer review, segregation of duties, security testing or release controls
- Promote specification-led development, reusable prompts and context, automated tests and evidence-based quality checks
- Coach engineering teams on effective and responsible use of AI tools and continuously improve the end-to-end engineering process

Azure Architecture & Platform Responsibilities

- Design the Azure resources required for each solution and validate designs directly in the Azure portal where appropriate
- Select suitable services across App Service, Container Apps, Functions, AKS, Azure OpenAI / Azure AI Foundry, Azure AI Search, Logic Apps, API Management, Service Bus, Event Grid, Storage, Azure SQL, PostgreSQL, Cosmos DB and Key Vault
- Design identity and access using Microsoft Entra ID, managed identities, role-based access control and least-privilege principles
- Define secure networking patterns using virtual networks, private endpoints, private DNS, firewalls and controlled ingress/egress
- Design environment and subscription structures, resource groups, naming, tagging, policy, secrets, configuration and deployment boundaries




- Ensure infrastructure is repeatable through Bicep and/or Terraform and delivered through Azure DevOps or GitHub Actions pipelines
- Build observability into the solution using Azure Monitor, Log Analytics and Application Insights, including actionable logs, metrics, traces and alerts
- Estimate and manage cloud consumption, model usage and automation run costs, and design appropriate budget, quota and scaling controls
- Align designs with the Azure Well-Architected Framework and organisational cloud governance standards

Required Qualifications

Experience

- Minimum 10 years in software engineering, systems integration or technology delivery, including at least 5 years in solution or technical architecture roles
- Proven experience leading complex solutions from early business discovery through production implementation and operational handover
- Demonstrated ability to translate ambiguous business problems into clear requirements, architecture decisions and executable engineering work
- Experience providing technical leadership to multidisciplinary development teams without relying solely on formal line authority
- Hands-on experience designing and deploying solutions on Microsoft Azure
- Experience delivering automation, AI-enabled applications, digital tools or integration-led solutions in an enterprise setting
- BSc/BA in Computer Science, Engineering or a related discipline, or equivalent practical experience

Technical Skills

- Solid solution architecture skills across application, integration, data, security, cloud and operational domains
- Strong understanding of APIs, event-driven architecture, microservices, asynchronous processing and integration patterns
- Working knowledge of modern software engineering practices, including source control, branching, CI/CD, automated testing, code review, security scanning and release management
- Practical knowledge of Azure compute, integration, data, identity, networking, security and monitoring services
- Ability to define non-functional requirements for security, privacy, availability, resilience, performance, scalability, maintainability and supportability
- Experience with infrastructure as code using Bicep and/or Terraform
- Ability to read and review code in one or more languages such as Python, TypeScript, C# or Java, and to challenge implementation choices constructively
- Understanding of data modelling, data quality, information lifecycle, integration contracts and access controls

AI & Automation Skills

- Practical understanding of large language models, RAG, embeddings, vector search, prompt design, tool/function calling and agent orchestration
- Experience designing guardrails and human-in-the-loop controls for AI-enabled or higher-risk automated processes
- Experience with Azure OpenAI / Azure AI Foundry or comparable enterprise AI platforms
- Hands-on experience using AI coding assistants and establishing quality controls for AI-generated code and artefacts
- Understanding of AI evaluation, safety testing, model and prompt versioning, observability and production monitoring
- Experience with workflow automation or low-code platforms such as Azure Logic Apps, Power Automate or equivalent

Preferred Qualifications

- Microsoft Certified: Azure Solutions Architect Expert (AZ-305) and/or Azure AI Engineer Associate (AI-102)
- Experience with Azure DevOps, GitHub Enterprise, GitHub Actions and GitHub Copilot governance
- Experience implementing multi-agent or tool-using agent solutions in a controlled enterprise environment
- Knowledge of Microsoft Power Platform, Dataverse, Copilot Studio or robotic process automation platforms
- Experience in regulated or security-conscious environments and familiarity with ISO 27001, SOC 2, GDPR, or comparable control frameworks
- Experience applying threat modelling, secure-by-design, Zero Trust and DevSecOps practices
- TOGAF or equivalent architecture certification
- Excellent facilitation, stakeholder management, written communication and technical leadership skills

What Success Looks Like

- Business problems are converted quickly into clear, proportionate and buildable solution designs
- Development teams understand what to build, why it is being built and the engineering standards they must meet
- AI and automation solutions progress from prototype to production without losing security, quality, control or supportability
- Azure resources are well designed, repeatable, observable and cost-conscious
- Architecture decisions reduce ambiguity, delivery risk and rework while improving time to value
- Reusable patterns and guardrails increase engineering speed across future solutions

📌 Solutions Architect (Delhi)
🏢 YNV Group
📍 Delhi