Backend AI Architect C2C Reston, VA

Role:
Backend AI Architect

Location:

Reston, VA (on-site)

Duration: 6+ months C2H

Mode:
Video

• 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 large language models 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 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, with AWS preferred;
  Azure or 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.

• 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 large language models and generative AI capabilities into customer-facing features and internal platforms.
• Establish specification-driven development workflows leveraging AI to improve developer productivity and quality.
• Partner with data science, machine learning 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.

• 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
• Large Language Models
• Generative AI
• Retrieval Augmented Generation
• AI Agents
• Inference Architecture
• Platform Engineering

• Describe your experience designing and scaling backend platforms using microservices or event-driven architectures.
• How have you integrated large language models or generative AI 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.