MTS, Post-Training (Enterprise)
Core
Delivering custom, high-performing LLMs to enterprise customers by managing the full post-training lifecycle from data curation to production deployment.
Role type
Senior Applied AI Engineer (Enterprise Post-Training)
Builds
Custom fine-tuned models, evaluation suites, and production data flywheels for enterprise clients.
Domain
Applied AI / Large Language Models / Enterprise Software
Deliverable
production ML models
Required skills
LLM post-training (fine-tuning, alignment), end-to-end evaluation suite design, regression risk management, production data curation, stakeholder engagement, open-weight model optimization
Preferred skills
Conversational/task-oriented agent training, LLM judge/reward model calibration, production data flywheel operations, open-weight model optimization (Llama, Qwen, Mistral, DeepSeek)
Technologies
Llama, Qwen, Mistral, DeepSeek, post-training frameworks, data processing pipelines
Responsibilities
Post-train and align open-weight and proprietary base models for complex business domains; Build bespoke evaluation suites and custom benchmarks; Curate and filter high-impact datasets combining production traces and synthetic augmentation; Manage and mitigate regression risks in model performance; Engage directly with enterprise stakeholders to translate requirements into technical metrics; Deploy models to meet strict enterprise latency, cost, and privacy targets
Seniority
Senior, hands-on IC