Senior Machine Learning Engineer - AMP
At a glance
Senior Machine Learning Engineering role at Valsoft Corporation. Beirut · full-time.
Pay not stated Machine Learning Engineer salary
aimlroles summary, based on the employer's posting.
What you'll do
- Build forecasting and rate suggestions for the auto-publish system, with limits tied to each property's tolerance
- Create LLM features for rate explanations, property-performance questions and automated market updates
- Turn competitor rate feeds and inconsistent third-party data into usable model signals
- Set up model checks and monitoring to spot declining performance before customers encounter it
- Deliver the connected APIs, data structures and UI that let revenue managers use these features
What you bring
- You have three to five years shipping production software, including substantial AI or machine-learning work
- Strong Python skills, plus the ability to use TypeScript or JavaScript in an application layer
- You have built with LLM APIs, including prompts, structured outputs, tools or retrieval, and ways to evaluate results
- You can work with SQL and data models, and have experience in forecasting, pricing optimization or recommendation systems
- You have deployed to the cloud and worked with messy real-world data and modern CI/CD
Who this fits
This suits an engineer who can take a problem from model or pipeline through production service and user-facing product, rather than focusing only on research or application development. You'll need to make decisions independently, communicate progress and blockers in writing, and maintain predictable timezone overlap with the team. The role is based onsite in Lebanon.
From the employer
Aspire Software is looking for a Senior Machine Learning 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 job:
You will build AI features end to end: the model or pipeline that produces the decision, the service that runs it, and the product surface where a revenue manager sees and acts on it. This is not a research role and it is not a pure app-dev role — it is the bridge between them.
Examples of what you'd own in your first year:
- Forecasting and rate-recommendation logic that feeds our auto-publish engine, including the guardrails that keep recommendations inside a property's tolerance LLM-driven features: natural-language explanations of why a rate moved, conversational querying of a property's performance, automated market commentary
- Rate-intelligence pipelines — ingesting competitor rates, cleaning messy third-party data, and turning it into signals the model can trust
- Evaluation harnesses and monitoring so we know when a model degrades before a customer does
- The full-stack surface around all of the above: APIs, data models, and the UI that exposes them
Requirements
- 3–5 years building and shipping production software, with meaningful AI/ML work in that time
- Strong Python; comfortable in TypeScript/JavaScript for the application layer
- Hands-on experience with LLM APIs (Anthropic, OpenAI or similar) — prompt design, structured
- output, tool use, retrieval, and the evaluation discipline to know whether any of it is working
- Solid SQL and data modeling; you can reason about a schema and write queries that don't melt the database
- Experience with at least one of: time-series forecasting, pricing/optimization models, or recommendation systems
- Comfort with messy, incomplete, real-world data — third-party feeds, inconsistent APIs, partial history
- Cloud deployment experience and familiarity with modern CI/CD
Nice To Have:
- Hospitality, travel, pricing, or marketplace domain experience
- Experience integrating with PMS, channel manager, or booking-engine APIs
- Background in a small startup where you owned a system rather than a ticket queue
How we work, and who succeeds here
- We hire for ownership as much as for stack. The engineers who do well:
- Take technical ownership. You're handed a problem, not a spec. You decide what to build, flag the trade-offs, and are accountable for the result in production — including when it breaks at an inconvenient hour.
- Are intrinsically driven. Nobody will watch your calendar. You choose the right next thing to work on when the direction is ambiguous, and you finish things.
- Communicate well asynchronously. Written updates that a busy reader can act on. You surface blockers early rather than going quiet. Clear, complete PR descriptions and issue comments.
- Manage your own time zone. You're responsible for maintaining reliable overlap with the team and for being predictable about when you're reachable.