Senior AI & Cloud-Native Software Architect

Jobgether; United States; Remote; USD 150,000 - 210,000; Full time

Benefits offered by this job

Remote work from multiple geographies

Part-time engagement

Hourly contract engagement

Immediate start

Technical leadership

Exposure to cutting-edge AI & cloud

End-to-end impact

Job summary

Jobgether is seeking an experienced Cloud-Native Architect to design scalable, secure AI-enabled architectures and lead engineering through architecture reviews and production deployment.

You will design GenAI/LMM solutions, implement RAG pipelines, and guide teams on best practices for reliability, latency, and cost optimization across distributed systems.

Qualifications

• 7+ years of software engineering with architecture responsibility.
• Experience owning production distributed systems architecture (microservices, REST, asynchronous messaging).
• Hands-on GenAI/LLM production experience including model integration and safety controls.
• Knowledge of RAG architectures, embeddings, and vector databases (pgvector/OpenSearch).
• Strong TS/Node.js or Python backend development skills.
• Deep understanding of cloud-native architectures (containers, Kubernetes, serverless, event-driven).
• Knowledge of cloud security, IAM, secrets handling, and data protection.
• Experience with Docker, CI/CD, testing, and observability tools.

Responsibilities

• Lead architectural decisions across the project lifecycle from discovery to production launch.
• Guide engineering teams through architecture reviews and code reviews.
• Design and implement observability practices (monitoring, logging, tracing, alerting).
• Build secure CI/CD pipelines and automated testing strategies.
• Translate business use cases into technical designs and executive summaries.
• Communicate trade-offs and participate in solution design decisions.

Skills

Architectural design

Cloud-native

GenAI integration

RAG pipelines

TypeScript/Node.js

Python

Kubernetes

Security

Observability

CI/CD

Tools

Docker

OpenTelemetry

Grafana

Datadog

Amazon CloudWatch

Terraform

AWS

pgvector

OpenSearch

Job description

Accountabilities

• Design scalable, secure, and resilient architectures for AI-enabled applications, defining service boundaries, data flows, APIs, and integration patterns.
• Establish cloud-native architectural approaches across microservices, containerized applications, serverless computing, and event-driven systems.
• Design, implement, and productionize GenAI and LLM solutions, including model selection and routing, retrieval strategies, evaluation frameworks, AI guardrails, and cost and latency optimization.
• Architect and develop RAG pipelines using embeddings, vector search, and appropriate data retrieval techniques to deliver relevant, reliable AI-generated responses.
• Build backend services using TypeScript/Node.js and/or Python, including asynchronous workflows, queues, workers, webhooks, and service integrations.
• Define non-functional requirements covering reliability, performance, scalability, security, and maintainability, evaluating architectural trade-offs and documenting key decisions through Architecture Decision Records (ADRs).
• Translate business use cases into technical designs, feasibility assessments, architecture recommendations, and executive-level summaries.
• Lead architectural decisions throughout the project lifecycle, from initial discovery and solution design to implementation, deployment, and production launch.
• Guide engineering teams through architecture reviews, code reviews, and technical discussions to ensure consistent standards and maintainable solutions.
• Design and implement observability practices using appropriate monitoring, logging, tracing, and alerting tools to improve system reliability and operational visibility.
• Build and maintain secure CI/CD pipelines, automated testing practices, and deployment workflows that support reliable and repeatable software delivery.
• Apply cloud security best practices across identity and access management, authorization, secrets management, data protection, and secure service-to-service communication.
• Identify opportunities to improve system architecture, operational efficiency, application resilience, and the overall quality of AI-enabled software.

Requirements:

• At least 7 years of software engineering experience, including significant responsibility for technical design and architectural decision-making.
• Proven experience owning the architecture of production distributed systems, including microservices, domain-driven design, REST APIs, and asynchronous communication patterns.
• Hands-on experience delivering production-grade GenAI or LLM applications, including model integration, evaluation, and AI safety controls.
• Practical knowledge of RAG architectures, embeddings, retrieval strategies, and vector databases such as pgvector, OpenSearch, or equivalent technologies.
• Strong software engineering skills in TypeScript/Node.js and/or Python, with the ability to design, implement, and maintain production backend services.
• Deep understanding of cloud-native architecture, including containers, Kubernetes, serverless computing, event-driven messaging, and distributed-system resilience patterns.
• Strong knowledge of cloud security principles, including identity management, authorization, secrets handling, and data protection.
• Experience with Docker, CI/CD pipelines, automated testing, and observability platforms or tools such as OpenTelemetry, Grafana, Datadog, or Amazon CloudWatch.
• Ability to evaluate architectural alternatives, define non-functional requirements, document technical decisions, and communicate trade-offs clearly.
• Experience guiding engineers through architecture and code reviews and taking ownership of solutions from initial design through production deployment.
• Strong analytical and problem-solving abilities, with the capacity to translate complex business needs into practical technical solutions.
• Excellent communication and collaboration skills, including the ability to explain technical concepts and present recommendations to both engineering teams and executive stakeholders.

Nice to have

• Experience with AWS services such as Bedrock, Lambda, API Gateway, SQS, EventBridge, Step Functions, ECS/EKS, Aurora PostgreSQL, and DynamoDB.
• Familiarity with conversational AI and contact center technologies, including WhatsApp Business API, Amazon Connect, Amazon Lex, Twilio, and voice bots.
• Experience with infrastructure-as-code tools such as Terraform, AWS CloudFormation, or AWS CDK.
• AWS Certified Solutions Architect - Professional certification or an equivalent cloud architecture qualification.
• Customer-facing consulting experience, including presenting technical assessments, architecture recommendations, and solution proposals to senior stakeholders.

Benefits:

• Remote work: Work remotely from Brazil, with flexibility in organizing your work within the engagement requirements.
• Part-time engagement: A part-time opportunity suited to an experienced professional seeking a focused architectural role.
• Contractor arrangement: Hourly contract engagement, with compensation based on the agreed contractual terms.
• Immediate start: The role is expected to begin as soon as possible.
• Technical ownership: Lead architecture decisions and contribute hands-on to the design and delivery of production AI and cloud-native applications.
• Cutting-edge technologies: Work with generative AI, LLMs, RAG, vector databases, AWS cloud services, and modern distributed architectures.
• End-to-end impact: Influence solutions from initial discovery and architectural design through implementation, production launch, and ongoing optimization.
• Technical leadership: Guide engineering teams, establish architectural best practices, and help improve software quality, security, and reliability.
• Compensation transparency: The position is paid hourly, but a specific rate or range was not provided in the original listing.
• Additional benefits: Healthcare, paid leave, equipment allowances, and other perks were not specified and should be confirmed during the application process.