Technical Account Manager (TAM)

Role overview A remote, North America-based opportunity for a Technical Account Manager who blends hands-on AI/ML expertise with customer success ownership. The role partners with enterprise and research customers post-sale, guiding onboarding, adoption, and expansion across the full machine learning lifecycle—from training and evaluation to agentic AI deployment.

Responsibilities - Own a portfolio of global accounts across financial services, insurance, pharma, transportation, and technology as the primary post-sale technical point of contact. - Lead onboarding, enablement, and technical working sessions covering workflows like time series forecasting, computer vision, LLM fine-tuning, RAG evaluation, and agentic AI development. - Debug Python SDK integrations, build working examples, and resolve escalations end-to-end alongside engineering teams. - Translate customer use cases and ML goals into platform-aligned strategies, including agentic and multi-agent systems. - Monitor customer health analytics to surface expansion opportunities and churn risks, partnering with sales on renewals. - Relay market and customer feedback to product and engineering to help shape roadmap priorities.

Requirements - 3+ years in a customer-facing AI/ML role such as TAM, Customer Success Engineer, Forward Deployed Engineer, or Solutions Architect. - Hands-on experience with LLM and agentic AI development, including frameworks and tools like OpenAI, LangGraph, Bedrock, or agent orchestration platforms. - Working knowledge of LLM observability concepts—tracing, evaluation datasets, LLM-as-a-judge, experiment tracking—and familiarity with OpenTelemetry or similar standards. - Strong Python skills for writing integration scripts, debugging customer code, and building sample workflows. - Demonstrated ability to manage a portfolio of accounts, juggle competing priorities, and own technical escalations from diagnosis to resolution. - Strong communication and presentation skills, with proven success engaging senior and executive stakeholders.

Nice to have - Cloud platform experience (AWS, Azure, GCP) and working knowledge of Docker and Kubernetes. - Familiarity with traditional ML frameworks such as PyTorch, TensorFlow, Keras, XGBoost, or Hugging Face. - Background supporting customers in regulated industries and their security, compliance, or model-risk requirements. - Open-source contributions or active participation in AI developer communities. - Bachelor's degree in Computer Science or a related engineering field, or equivalent practical experience.

Benefits and work setup - Fully remote within North America, collaborating with a globally distributed team. - Competitive base plus variable compensation, comprehensive benefits, and flexible working hours. - Quarterly travel expected for on-site customer engagement. - Growth opportunities and exposure to cutting-edge ML, MLOps, and generative AI projects.