AI Engineer - KDR

Aspire Software is looking for an AI/ML Engineer 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 Role

You'll be working inside a legacy codebase with established architecture, business rules, and customer commitments, and your job is twofold:

(1) use AI tooling to extend and modernize what these platforms can do, and

(2) use that same AI expertise to make our existing engineering teams faster and sharper — compressing the time between a customer requirement and a shipped, correct solution.

You will work directly with engineering leads, product, and operators to find where AI-assisted development — code generation, automated testing, requirement-to-implementation acceleration, intelligent tooling — can shorten delivery cycles without sacrificing quality, especially under aggressive customer timelines. If you enjoy going deep into complex, configurable systems and making both the product and the team around it faster, this role is for you.

Your mission: use AI to multiply the output and accuracy of our development teams, while extending the intelligence of the platforms themselves.

Primary Responsibilities

Accelerate team delivery

• Introduce and operationalize AI-assisted development practices (code generation, AI pair-programming, automated test generation) across existing engineering teams.
• Identify friction points in how requirements move from customer ask to shipped feature, and apply AI tooling to close the gap.
• Partner with developers working in Angular, React, C#, and TypeScript to embed AI tooling into their actual day-to-day workflow — not a side project.
• Build reusable patterns/playbooks so AI-assisted delivery scales across teams, not just your own output.

Extend platform intelligence

• Design, develop, and deploy AI features directly into existing, deeply configurable SaaS platforms.
• Integrate LLMs and ML models into legacy architectures without destabilizing existing functional/technical requirements.
• Build automation pipelines that reduce manual operational tasks within the product and internally.
• Develop intelligent agents and AI-powered workflow systems where they create measurable leverage.

Architect AI Infrastructure

• Implement RAG systems and embedding pipelines.
• Work with vector databases and retrieval systems.
• Build AI orchestration layers and agent frameworks.
• Ensure reliability, monitoring, and evaluation of AI outputs.
• Deploy and maintain models in production environments.

Collaborate cross-functionally

• Work directly with engineering leads and non-technical stakeholders to understand both platform constraints and business pain points.
• Translate ambiguous problems — in the product or in the development process — into scalable AI solutions.
• Balance speed with engineering rigor, especially against aggressive customer-driven timelines.
• Drive execution with minimal oversight.

Core engineering

• Experience working within deeply configurable, enterprise-grade SaaS platforms (not greenfield products).
• Strong Python expertise (required).
• Comfortable across multiple languages/frameworks — Angular, React, C#, and TypeScript.
• Backend development experience (APIs, microservices) in a mature production codebase.
• Experience deploying production systems; familiarity with cloud infrastructure (AWS, Azure, or GCP).
• CI/CD, Docker, containerization.

AI & ML systems

Experience with:

• LLM integration (OpenAI, Anthropic, open-source models, etc.)
• AI-assisted development tooling (code generation / coding copilots, applied at team scale, not just individually)
• Prompt engineering
• RAG pipelines
• Vector databases (Pinecone, Weaviate, FAISS, etc.)
• Embeddings and semantic search

Familiarity with:

• Model fine-tuning (where relevant)
• Supervised / unsupervised learning
• Evaluation frameworks for LLM systems
• Understanding of hallucination mitigation, guardrails, and output validation.

Data & automation

Experience with:

• Building data pipelines and ETL processes.
• Working with structured & unstructured data.
• API integrations across SaaS platforms.
• Automation tooling and workflow orchestration.

MLOps & productionization

Experience with:

• Model versioning and experiment tracking.
• Monitoring model performance.
• Logging and observability.
• A/B testing AI features.

You'll Probably Love This Role If…

• Have worked inside complex, long-lived SaaS codebases and know how to introduce change.
• Enjoy autonomy and ownership.
• Thrive in fast-moving environments with real customer deadlines.
• Want to make both a platform and a team measurably faster.

Required Qualifications

• Deep comfort working inside legacy, configurable enterprise platforms.
• Highly hands-on — you ship, not just design.
• Multiplier mindset — as interested in making the team faster as in making the product smarter.
• Comfortable with ambiguity and aggressive timelines.
• Business-oriented — you care about ROI and customer-facing impact.
• Pragmatic — uses the right tool, not the fanciest one