Recruitis
Tech Lead, Data Scientist (LLM)
- Compensation
- Not disclosed
- Location
- Bangkok
- Work setup
- Not specified
- Seniority
- Lead
- Employment
- Not specified
- Experience
- Not specified
- Category
- Data science
- Posted
Skills
Must have
- Strong hands-on experience in Data Science, Applied Machine Learning, or a closely related technical field, with ownership of production systems.
- Experience building and operating real-world LLM systems in production, including areas such as RAG, agent or tool orchestration, prompt or context design, memory handling, or system-level evaluation.
- Strong foundation in machine learning, statistics, experimentation, modeling, feature engineering, and analytical problem solving.
- Demonstrated ability to move from ambiguity to implementation by designing practical solutions for evolving product or business problems.
- Experience working closely with product managers and software engineers to take AI systems from concept to production.
- Strong understanding of evaluation, testing, iteration, and performance measurement for applied ML / LLM systems.
- Ability to balance product impact with technical trade-offs such as model quality, latency, cost, and operational reliability.
- Strong communication and stakeholder management skills, with the ability to explain technical concepts in a clear and structured way.
- Comfort operating in fast-paced, evolving environments where priorities, requirements, and system behavior may change quickly.
- Experience leading complex technical workstreams
Full description
Who is our client?
Our client is a large-scale technology organization with a strong focus on engineering, data, and AI. They are continuing to invest in AI/ML platform capabilities that enable teams to build, operate, and scale machine learning and intelligent applications more effectively.
About the Role
We are hiring a Lead LLM Data Scientist to help design, build, and evolve a production-grade AI assistant experience. This is a highly hands-on role for someone who can turn ambiguous product problems into deployable ML / LLM systems, while working closely with product managers, engineers, and senior stakeholders. You will operate at the intersection of data science, applied machine learning, LLM system design, experimentation, and product delivery.
*Relocation sponsorship is provided
Responsibilities
- Own end-to-end design and development of LLM-based systems, from problem framing and solution design through deployment, iteration, and long-term improvement.
- Translate open-ended product or user problems into robust ML / LLM solutions that balance quality, latency, reliability, maintainability, and cost.
- Design and improve production AI workflows involving areas such as retrieval, reasoning, orchestration, tool use, memory, ranking, personalization, or multi-step task execution.
- Build evaluation frameworks, testing approaches, and monitoring mechanisms to assess model quality, failure modes, system behavior, and production performance.
- Run structured experiments and use real-world feedback, behavioral signals, and performance metrics to improve model and system outcomes over time.
- Work closely with engineering partners to productionize solutions and ensure systems are scalable, observable, and reliable in live environments.
- Partner with product and cross-functional stakeholders to make informed trade-offs and shape product direction through technical insight and data-driven decision-making.
- Provide technical leadership in ambiguous problem spaces, helping define solution direction, execution priorities, and practical paths to delivery.
- Communicate complex technical concepts, design choices, and experimental findings clearly to both technical and non-technical stakeholders.
- Contribute as a senior individual contributor who can operate with high ownership and influence product, technical design, and implementation quality.
Qualifications
- Strong hands-on experience in Data Science, Applied Machine Learning, or a closely related technical field, with ownership of production systems.
- Experience building and operating , including areas such as RAG, agent or tool orchestration, prompt or context design, memory handling, or system-level evaluation.