Lead AI Engineer (Product, Automation & Deployment)

About the role

Virtelligence is seeking a Lead AI Engineer to orchestrate AI‑driven development, automation, and deployment of enterprise software. You will act as a high‑level pilot, leveraging state‑of‑the‑art large language models and AI agents to rapidly build, modify, test, and secure production‑ready applications.

Key responsibilities

• Use AI‑native developer environments to generate, refactor, and extend backend logic, database schemas, and internal scripts.
• Perform human‑in‑the‑loop code auditing to identify structural bugs, logical errors, and security vulnerabilities before production.
• Design and maintain multi‑LLM routing architecture that selects models (Claude Code, Gemini AntiGravity, GLM/Zhipu) based on cost, latency, and performance.
• Direct AI agents to create exhaustive unit and integration tests, keeping codebases clean with minimal manual effort.
• Own dual‑cloud deployment, containerization, and networking across AWS and Google Cloud Platform.
• Leverage AI tooling to draft and maintain infrastructure‑as‑code (Docker, Terraform) and CI/CD pipelines.

Required profile

• Deep, hands‑on mastery of modern AI‑assisted development tools and agents.
• Strong experience as an API engineer building, deploying, and scaling production‑ready applications that integrate large language models.
• Ability to design robust orchestration workflows, Retrieval‑Augmented Generation pipelines, and autonomous agents using frameworks such as LangChain and LangGraph.
• Expertise in prompting, model literacy, token optimization, and structured output across multiple model ecosystems.

Required skills

• AI‑assisted development tools: Cursor, GitHub Copilot, Aider, Windsurf, Claude Code.
• Large language model APIs: Claude Code, Gemini AntiGravity, GLM/Zhipu.
• Cloud platforms: Amazon Web Services (AWS), Google Cloud Platform (GCP).
• Containerization & orchestration: Docker.
• Infrastructure‑as‑code: Terraform.
• CI/CD pipeline creation and maintenance.
• Backend frameworks: FastAPI.
• AI orchestration frameworks: LangChain, LangGraph.
• Database and security knowledge: SQL, detection of injection vulnerabilities.