{"id":888274,"url":"https://alion.io/job/recruitis-tech-lead-data-scientist-llm","title":"Tech Lead, Data Scientist (LLM)","company":{"id":688309,"name":"Recruitis","domain":"recruitis.io","url":"https://alion.io/company/recruitis","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Greenhouse","truth_index":{"grade":"D","score":52,"open_postings":10,"ghost_share":0.8,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":null,"computed_at":"2026-11-08T05:47:12Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"lead","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Bangkok, Thailand"],"countries":["TH"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":23000,"max_usd":57000,"period":"year","method":"global_role_cell_scaled_by_country","sample_n":1286},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Function Calling","optional":false},{"name":"LLM","optional":false},{"name":"Machine Learning","optional":false},{"name":"RAG","optional":false}],"status":"live","first_seen_at":"2026-03-06T04:48:06Z","employer_posted_date":"2026-03-06","last_verified_at":"2026-11-09T03:01:58Z","board_verified":true,"closed_at":null,"days_open":247,"trust":{"level":"ghost","repost_count":0,"flags":["stale","company_stale"],"days_open":247},"description":"Who is our client?\nOur 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.\nAbout the Role\nWe 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.\n*Relocation sponsorship is provided\nResponsibilities\nOwn end-to-end design and development of LLM-based systems, from problem framing and solution design through deployment, iteration, and long-term improvement.\nTranslate open-ended product or user problems into robust ML / LLM solutions that balance quality, latency, reliability, maintainability, and cost.\nDesign and improve production AI workflows involving areas such as retrieval, reasoning, orchestration, tool use, memory, ranking, personalization, or multi-step task execution.\nBuild evaluation frameworks, testing approaches, and monitoring mechanisms to assess model quality, failure modes, system behavior, and production performance.\nRun structured experiments and use real-world feedback, behavioral signals, and performance metrics to improve model and system outcomes over time.\nWork closely with engineering partners to productionize solutions and ensure systems are scalable, observable, and reliable in live environments.\nPartner with product and cross-functional stakeholders to make informed trade-offs and shape product direction through technical insight and data-driven decision-making.\nProvide technical leadership in ambiguous problem spaces, helping define solution direction, execution priorities, and practical paths to delivery.\nCommunicate complex technical concepts, design choices, and experimental findings clearly to both technical and non-technical stakeholders.\nContribute as a senior individual contributor who can operate with high ownership and influence product, technical design, and implementation quality.\nQualifications\nStrong hands-on experience in Data Science, Applied Machine Learning, or a closely related technical field, with ownership of production systems.\nExperience 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.\nStrong foundation in machine learning, statistics, experimentation, modeling, feature engineering, and analytical problem solving.\nDemonstrated ability to move from ambiguity to implementation by designing practical solutions for evolving product or business problems.\nExperience working closely with product managers and software engineers to take AI systems from concept to production.\nStrong understanding of evaluation, testing, iteration, and performance measurement for applied ML / LLM systems.\nAbility to balance product impact with technical trade-offs such as model quality, latency, cost, and operational reliability.\nStrong communication and stakeholder management skills, with the ability to explain technical concepts in a clear and structured way.\nComfort operating in fast-paced, evolving environments where priorities, requirements, and system behavior may 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