Solutions Architect

Incedo Inc.; New York (NY); On-site; USD 180,000 - 260,000; Full time

Job summary

Incedo Inc. is seeking a seasoned Solution Architect to lead AI-native and cloud-first initiatives for enterprise applications. You will define end-to-end architectures using .NET, multiple clouds, and AI services, collaborating with clients and stakeholders to deliver scalable, secure solutions.

Responsibilities include architecture governance, migration planning, and embedding responsible AI practices while guiding teams through modern software delivery, DevOps, and performance optimization.

Qualifications

• 12+ years of software engineering, application development, and solution architecture.
• Hands-on with C#, .NET Core, ASP.NET Core, and RESTful Web APIs.
• Enterprise-scale, microservices-based architecture experience.
• Cloud platform experience: AWS, Azure, or GCP.
• Experience integrating Generative AI/LLMs into apps.
• AI-native architecture understanding (LLM integration, prompts, workflows).
• API design, integration, messaging, event-driven systems.
• Security, identity, scalability, resilience, high availability.
• CI/CD, automated testing, DevOps.
• Excellent communication and leadership.

Responsibilities

• Define end-to-end solution architecture for enterprise apps using .NET, cloud, and AI-native design.
• Translate requirements into scalable, secure AI-enabled solutions.
• Create SAD, HLD, LLD, and technical specifications.
• Establish architectural standards, design patterns, 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 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.
• Design AI-native applications that integrate intelligence into core workflows.
• Integrate Generative AI and LLM capabilities into enterprise applications.
• Build AI-powered services, copilots, and intelligent workflow automation solutions.
• Design and implement Retrieval-Augmented Generation (RAG) solutions.
• Evaluate AI agent frameworks, tool calling, and MCP where appropriate.
• Integrate AI capabilities using major cloud AI services.
• Apply responsible AI practices and governance.
• Design for observability, latency, reliability, and cost optimization.
• Establish reusable AI integration patterns and governance.
• Act as primary technical interface for clients and stakeholders.

Skills

C#

.NET Core

ASP.NET Core

RESTful APIs

Microservices

Cloud platforms

AI integration

DevOps

Solution architecture

Leadership

Tools

Docker

Kubernetes

Terraform

CloudFormation

Bicep

Job description

Location : NYC City

Duration : Full Time

About Incedo:

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”.

Engineering & Operations excellence

Key Responsibilities

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.

3. AI-Native Architecture & Engineering

• Design and implement AI-native applications that integrate intelligence into core business workflows rather than treating AI as a standalone add-on.
• 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.

4. Security, Performance & Reliability

• 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.

5. 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.

Required Qualifications

• 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.

Preferred Qualifications

• 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