Solutions Architect

1. Solution Architecture & Technical Leadership

• Define end-to-end solution architecture for enterprise applications using .NET technologies, cloud platforms, and AI-native design principles.
• Translate business requirements into scalable, secure, maintainable, and AI-enabled technical solutions.
• Create and review Solution Architecture Documents (SAD), High-Level Designs (HLD), Low-Level Designs (LLD), and technical specifications.
• Establish architectural standards, design patterns, coding guidelines, and engineering best practices.
• Lead architecture reviews, technical design sessions, and evaluations of emerging technologies.
• Identify opportunities to modernize existing applications using cloud-native and AI-enabled capabilities.
• Design and implement cloud-native solutions using AWS, Microsoft Azure, or Google Cloud Platform.
• Define architecture for compute, storage, networking, databases, security, monitoring, and disaster recovery.
• Leverage cloud services such as AWS Lambda, ECS/EKS, S3, and RDS; Azure Functions, AKS, Azure SQL, and Azure AI services; or GCP Cloud Run, GKE, Cloud SQL, and Vertex AI.
• Design cloud migration and application modernization strategies.
• Implement Infrastructure as Code using Terraform, CloudFormation, Bicep, or equivalent tools.
• Optimize cloud environments for scalability, availability, performance, security, and cost efficiency., * Integrate Generative AI and Large Language Models (LLMs) into .NET applications using suitable cloud AI services and model APIs.
• Build AI-powered services, copilots, conversational interfaces, and intelligent workflow automation solutions.
• Design and implement Retrieval-Augmented Generation (RAG) solutions using enterprise data sources, embeddings, and vector databases.
• Evaluate and apply AI agent frameworks, tool calling, and Model Context Protocol (MCP) where appropriate.
• Integrate AI capabilities using services such as Azure AI Foundry, Azure OpenAI, Amazon Bedrock, Amazon SageMaker, or Google Vertex AI.
• Apply responsible AI practices, including data privacy, access control, model evaluation, guardrails, and protection against prompt injection.
• Design for AI observability, response quality, latency, reliability, and inference cost optimization.
• Establish reusable AI integration patterns, architectural standards, and governance practices., * Implement secure application design using OAuth 2.0, OpenID Connect, JWT, and role-based access control.
• Apply cloud security best practices, identity and access management, encryption, and secrets management.
• Ensure appropriate security controls for enterprise data accessed by AI models and applications.
• Design systems for high availability, fault tolerance, disaster recovery, and business continuity.
• Establish monitoring, logging, distributed tracing, and alerting practices.
• Address AI-specific risks, including sensitive data exposure, hallucinations, unauthorized tool execution, and prompt injection.

1. Client & Stakeholder Engagement

• Act as the primary technical interface for clients and business stakeholders.
• Lead architecture discussions, technical workshops, and solution presentations.
• Identify opportunities to apply AI and cloud technologies to solve business problems.
• Explain architectural decisions, trade-offs, and AI adoption considerations to technical and non-technical audiences.
• Support estimation, technical planning, risk assessment, and delivery governance.

• 12+ years of experience in software engineering, application development, and solution architecture.
• Strong hands-on experience with C#, .NET Core, ASP.NET Core, and RESTful Web APIs.
• Proven experience designing enterprise-scale applications using microservices and distributed architectures.
• Strong architecture experience in at least one major cloud platform: AWS, Azure, or GCP.
• Practical experience integrating Generative AI or LLM capabilities into enterprise applications.
• Understanding of AI-native application architecture, including LLM integration, prompt engineering, and AI-powered workflows.
• Experience with API design, system integration, messaging, and event-driven architecture.
• Strong understanding of application security, identity management, scalability, resilience, and high availability.
• Experience with CI/CD, automated testing, and DevOps practices.
• Excellent communication, stakeholder management, problem-solving, and technical leadership skills., * Experience with two or more cloud platforms among AWS, Azure, and GCP.
• Hands-on experience with RAG, embeddings, vector databases, AI agents, or tool calling.
• Experience with MCP or AI orchestration frameworks.
• Familiarity with AI application evaluation, guardrails, observability, and responsible AI practices.
• Experience using AI coding assistants such as GitHub Copilot or Cursor in professional software engineering.
• Experience with Docker, Kubernetes, Terraform, CloudFormation, or Bicep.
• Knowledge of Kafka, RabbitMQ, AWS SQS/SNS, Azure Service Bus, or GCP Pub/Sub.
• Relevant cloud, AI, or solution architecture certifications.
• Experience in financial services, banking, insurance, healthcare, or other regulated industries.
• Experience leading distributed engineering teams and large-scale modernization initiatives.
• Enterprise solution architecture and .NET technical leadership
• Cloud-native architecture and application modernization
• AI-native application design and Generative AI integration
• Microservices, APIs, and event-driven architecture
• AI security, governance, and responsible implementation
• DevOps, engineering productivity, and automation
• Performance, scalability, reliability, and cost optimization
• Client engagement, communication, and technical mentoring

Incedo Inc. is a high-growth Digital, Data and AI Transformation Specialist firm headquartered in New Jersey. We are a long-term strategy execution partner for Fortune 500 enterprises, operating at the intersection of business and technology across Banking & Payments, Wealth Management, Telecom, Hi-Tech, and Life Sciences.

We are building Incedo 4.0 - an AI-native, execution-focused, founder-led organization designed for scale, speed, and long-term impact.

Incedo delivers ROI from AI @ Scale through the “Power of 3”.