- 10+ years of professional software development experience.
Strong background in backend engineering, software architecture, and system design.
Strong hands-on experience building and shipping production AI solutions.
AI agents and agentic workflows;
MCP / Model Context Protocol integrations;
LLM-based applications and integrations.
Python;
TypeScript / Node.js;
Go;
Java;
or another comparable backend technology.
Experience designing and managing cloud infrastructure using AWS, GCP, or Azure.
Strong understanding of AI security, including credential management, access controls, prompt injection risks, and sensitive data protection.
Experience supporting or leading AI adoption within engineering teams.
Ability to translate technical AI concepts into practical solutions aligned with business needs.
Excellent communication and leadership skills.
- English: Advanced/ Native-level proficiency.
These requirements follow the source profile, including the 10+ years of engineering background, language-agnostic backend requirement, production MCP/agentic tooling, cloud infrastructure, AI security, and C2/native English.
Experience with AI FinOps / LLM cost optimization, including semantic caching, rate limiting, and cost-aware model routing.
Knowledge of MCP standards and specifications.
Experience with frameworks such as LangChain or LlamaIndex.
Docker;
Terraform / Infrastructure as Code;
CI/CD;
observability and monitoring tools such as Datadog or ELK.