.Net Backend AI Architet

.NET Backend AI Architect

Job Overview

Core Back End Architecture
• Strong experience architecting scalable, secure, and observable distributed systems, including microservices and event driven architectures.
• Proven expertise defining service architecture strategies, including APIs, data contracts, and runtime platforms across multiple teams.
• Deep understanding of system design fundamentals such as consistency models, caching strategies, resilience patterns, and fault tolerance.
• Hands on experience with at least one major backend ecosystem: Node.js/TypeScript, Java/Kotlin, .NET, Python, or Go.
• Strong background in operational excellence, including observability, performance tuning, incident response, and reliability engineering.
• Experience partnering with product, security, SRE, and data teams to translate business requirements into resilient technical solutions.

AI Driven & Intelligent Platform Skills
• Experience integrating or developing with LLMs and Generative AI services within enterprise platforms.
• Strong understanding of prompt engineering, evaluation techniques, and AI quality metrics.
• Experience architecting inference systems, including routing, batching, caching, and cost optimization.
• Ability to design intelligent service flows, including Retrieval Augmented Generation (RAG) and agent based architectures.
• Familiarity with AI safety, governance, and responsible AI principles.
• Experience developing AI powered platform components, such as intelligent API gateways, policy engines, or observability assistants.
• Knowledge of AI driven analytics and telemetry for monitoring model performance and service health.

Cloud, Integration & Platform Engineering
• Experience with cloud platforms (AWS preferred; Azure/Google Cloud Platform acceptable).
• Strong understanding of API gateways, service mesh, and networking fundamentals.
• Hands on experience with data and streaming technologies such as SQL, NoSQL, Kafka, and Redis.
• Experience with CI/CD pipelines, infrastructure as code, automated testing, and progressive delivery strategies.
• Solid grounding in security fundamentals, including threat modeling, identity, encryption, and secure by default design patterns

Roles & Responsibilities
Back End Architecture Leadership
• Architect and design enterprise scale backend platforms that are secure, highly available, performant, and cost efficient.
• Define and govern service architecture standards, API contracts, and runtime patterns across development teams.
• Drive technical decisions that balance innovation, maintainability, operability, and long term platform health.
• Mentor and guide engineering teams in implementing services, APIs, and shared platform capabilities.

AI Augmented Platform Architecture
• Define and implement AI augmented backend architectures, including inference aware service patterns and model serving strategies.
• Integrate LLMs and GenAI capabilities into customer facing features and internal platforms.
• Establish spec driven development workflows leveraging AI to improve developer productivity and quality.
• Partner with Data Science, ML Engineering, and Product teams to operationalize models with strong SLAs, security, and cost controls.
• Evaluate emerging AI frameworks and align solutions with enterprise standards and governance models.

Developer Experience & Engineering Excellence
• Champion Developer Experience (DevEx) by improving local development tooling, CI/CD quality gates, and test automation.
• Establish best practices for coding standards, secure development, performance optimization, and reliability engineering.
• Promote observability first design using logs, metrics, traces, and AI assisted insights.
Generic Managerial Skills, If any
• Strong stakeholder management with the ability to communicate effectively at executive, product, and engineering levels.
• Proven experience leading large, matrixed, multi-vendor teams.
• Ability to balance strategic vision with hands-on architectural depth.
Key Words to search in Resume
• Back End Architect, Distributed Systems, Microservices, Event Driven Architecture, API Design, Cloud Native, AWS, DevEx, CI/CD, Observability, LLM, GenAI, RAG, AI Agents, Inference Architecture, Platform Engineering