Our Customer:
Our customer is developing an AI-powered platform that helps organizations accelerate innovation through advanced artificial intelligence, secure infrastructure, and modern backend engineering. The team builds scalable, production-grade AI solutions designed to operate across cloud and private environments while maintaining high standards of security, reliability, and performance.
Your Tasks:
Design, develop, and maintain scalable backend services, APIs, and system architecture for AI-powered applications.
Take full ownership of features throughout the entire development lifecycle, from technical design and architecture to implementation, deployment, monitoring, and continuous improvement.
Transform AI concepts, Large Language Model (LLM) capabilities, and intelligent agent designs into reliable, production-ready backend systems.
Design and implement AI reasoning workflows, agent orchestration, and integrations with enterprise services, external platforms, and internal data sources.
Build, maintain, and optimize CI/CD pipelines across cloud and secure private deployment environments.
Ensure software quality through engineering best practices, self-review, automated testing, observability, and continuous improvement.
Integrate LLMs into enterprise applications while ensuring security, scalability, and reliable production performance.
Contribute to infrastructure design for secure AI deployments, including private, on-premises, and self-hosted environments.
Collaborate closely with AI engineers, QA specialists, and cross-functional teams to deliver robust, production-grade AI solutions.
Work independently with a high level of ownership while contributing to collaborative engineering initiatives and continuously raising technical standards.
Required Experience and Skills:
6+ years of experience as a Backend or Systems Engineer with strong expertise in backend architecture and system design.
Hands-on experience building and deploying production-grade LLM applications or AI agent solutions.
Strong experience designing and developing APIs, backend services, distributed systems, and scalable application architectures.
Experience working with relational and/or NoSQL databases in production environments.
Strong understanding of system reliability, scalability, observability, and performance optimization.
Experience building and maintaining CI/CD pipelines and modern software delivery workflows.
Experience working with Google Cloud Platform (GCP) and deploying cloud-native applications.
Familiarity with secure private infrastructure, on-premises deployments, or hybrid cloud environments.
Strong understanding of application security, API security, data protection, and privacy best practices.
Proven ability to independently own technical solutions from architecture and implementation through production deployment and ongoing support.
Experience collaborating effectively with AI engineers, QA teams, and cross-functional engineering teams in Agile environments.
Excellent problem-solving, communication, and technical documentation skills.
Fluent English.
Would Be a Plus:
Experience working with open-weight or self-hosted AI models.
Experience designing infrastructure for private AI deployments, including compute and operational considerations.
Knowledge of knowledge graphs or graph-based data modeling.
Background in algorithmic problem-solving or data science.
Experience building personal software projects or contributing to open-source initiatives.
Working Conditions:
5-day working week, 8-hour working day.
Remote work.
