Sr. AI Engineer (พร้อมเริ่มงาน)
Summary
Senior AI engineer at Promes in Bangkok who leads data science projects end-to-end — data gathering, ML modeling, analysis and reporting — while managing and mentoring a small team. Core stack includes ML frameworks (Scikit-Learn, PyTorch, Hugging Face), Kubernetes-based data systems, plus pre-sale, waterfall PM and budgeting duties.
Salary: ฿50,000 – ฿70,000 per month
Responsibilities
Delivers quickly with minimal direction or oversight; responsible for end-to-end, good-quality delivery of complex projects
Oversee the entire lifecycle of data science projects, including data gathering, processing, analysis, and reporting
Leads by example, demonstrating good teamwork and being a good teammate
Manages the work of a small team, generally focused on a particular business or product area or technical function
Mentors, pair programs, and conduct code reviews
Helps to unblock, debug, and discuss statistical, machine learning, software engineering, and data issues
(Co-)lead the design and development plan of the whole project
Be able to (co-)lead the pre-sale strategy from the beginning to the solid project
Understand the budgeting allocation and expected revenue
Leads and influences peers across all teams and functions
Qualifications
At least 4-5 years of experience
Fluent in English: read, write, listen and speak
Proven track record of managing and executing data science (or big data) projects
Have a deep understanding of two or three field areas: text processing, speech processing, information retrieval, time-series, and geospatial knowledge
Has solid knowledge of waterfall project management
Basic finance knowledge for the project-wise consideration
Pre-sale skills: sophisticated in solution mapping and architecture, TOR drafting
Demonstrates resilience and adaptability in handling unexpected challenges and high-pressure situations.
ML & Stats
Help junior folks verify their hypothesis (both theoretically and non-theoretical factors) and the setup for testing
Must have a solid understanding of machine learning from traditional to neural network
Deeply understands the internal workings of one or more ML frameworks: Scikit-Learn, PyTorch, Huggingface, etc.
Be able to learn new knowledge and their landscape when entering a new domain
Software Engineering and Data Systems
Be able to conduct pair programming and code review with the coding principles by “reasonably” describing the option of coding pattern/skeleton that has selected
Understand process concurrency and experienced in asynchronous implementation
Can distinguish between stateless and stateful applications
Fluent in Kubernetes usage and understand the important components: Deployment, Service, Ingress, Statefulset, Persistent Volume (PV), and Persistent Volume Claim (PVC)
Get the idea of the CAP theorem
Exploit transactional database, data warehouse, and data lake for specific use cases