AI Engineering Manager - ANJU
At a glance
Manager-level AI Engineering role at Valsoft Corporation. Beirut · full-time.
Pay not stated AI Engineering salary
aimlroles summary, based on the employer's posting.
What you'll do
- Set the product’s technical design, including components that use AI or large language models
- Build systems that scale while protecting security, maintainability, reliability, and cost efficiency
- Guide developers and QA engineers, setting expectations for AI use in coding and reviews
- Own dependable releases and ensure AI features are tested, measurable, and ready for production
- Improve engineering speed with AI-assisted tools and automation in testing and CI/CD
What you bring
- At least seven years in software engineering, including two or more years leading technical work
- Practical experience creating and launching systems that use AI
- Strong command of LLM integration, prompt design, RAG, vector search, and AI orchestration frameworks
- Experience with Azure, AWS, or GCP, plus scalable distributed-system design
Who this fits
This role suits a hands-on technical leader who can guide developers and QA while owning architecture and delivery. It is based onsite in Beirut, Lebanon.
From the employer
Aspire Software is looking for an AI Engineering Manager to join our team in Lebanon.
Here is a little window into our company: Aspire Software operates and manages wholly owned software companies, providing mission-critical solutions across multiple verticals. By implementing industry best practices, Aspire delivers a time sensitive integration process, and the operation of a decentralized model has allowed it to become a hub for creating rapid growth by reinvesting in its portfolio.
About the job:
The AI Engineering Manager leads a cross-functional team (Development & QA) and owns technical architecture, delivery outcomes, AI-enabled system design and driving an AI first culture among your team. This role is both strategic and hands-on. It requires strong expertise in modern AI frameworks, applied AI architectures, and scalable system design. The manager is accountable for delivering stable, secure, AI-powered capabilities — not just managing execution.
Core Responsibilities
Technical Leadership & Architecture
- Own product architecture, including AI/LLM-integrated components
- Design scalable, secure, and maintainable systems
Lead decisions involving:
- LLM integration (OpenAI, Azure OpenAI, etc.)
- RAG architectures and vector databases
- AI orchestration frameworks (LangChain, Semantic Kernel, LlamaIndex)
- Ensure system reliability, observability, and cost efficiency
- Establish AI governance and safe usage practices
AI Framework & Methodology Expertise
- Apply modern AI methodologies, including:
- Retrieval-Augmented Generation (RAG)
- Prompt engineering and evaluation
- AI output validation and guardrails
- Human-in-the-loop workflows
- Model monitoring and performance evaluation
- Drive experimentation, A/B testing, and telemetry-based decision making
Team Leadership
- Lead and mentor developers and QA engineers
- Raise AI literacy across the team
- Establish AI-native coding and review standards
- Balance speed, quality, and architectural integrity
Delivery & Outcome Ownership
- Own predictable, high-quality releases
- Ensure AI features are measurable, validated, and production-ready
- Act as technical escalation point
- Drive cross-team alignment with Product, Data, and DevOps
AI-Driven Engineering Excellence
- Leverage AI-assisted development tools to improve velocity
- Optimize AI cost-performance tradeoffs
- Embed automation into testing and CI/CD pipelines
Requirements
- 7+ years software engineering experience, 2+ years in technical leadership
- Hands-on experience building and deploying AI-enabled systems
Strong knowledge of:
- LLM integration and prompt design
- RAG architectures and vector search
- AI orchestration frameworks
- Cloud platforms (Azure, AWS, or GCP)
- Experience designing scalable distributed systems
Preferred
- Experience in regulated industries
- AI governance and compliance knowledge
- Model lifecycle management and monitoring tools