Backend AI Architect

Backend AI Architect

Requires Total

Experience:

12-14 Years.

NOTE:

MUST HAVE .NET EXP. Please share new profiles with exp in .Net

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/Type Script, 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.

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.

Experience with cloud platforms (AWS preferred; Azure/GCP acceptable). Strong understanding of API gateways, service mesh, and networking fundamentals. Hands on experience with data and streaming technologies such as SQL, No

SQL, 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.

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.

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.

Champion Developer Experience (Dev Ex) 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.

Back End Architect, Distributed Systems, Microservices, Event Driven Architecture, API Design, Cloud Native, AWS, Dev Ex, CI/CD, Observability, LLM, GenAI, RAG, AI Agents, Inference Architecture, Platform Engineering

• Describe your experience designing and scaling backend platforms using microservices or event driven architectures.
• How have you integrated LLMs or GenAI capabilities into enterprise backend systems?
• Explain your experience with inference architecture (routing, caching, cost optimization).
• Describe how you have improved developer experience, CI/CD quality, or platform reliability in past roles.