Top AI & Machine Learning Jobs

Architect and develop production-grade GenAI systems, including LLM-powered agents, RAG pipelines, tool integrations, multi-agent coordination, memory systems, and chatbot experiences. Lead AI productization across engineering, product, and research teams; establish architecture, infrastructure, observability, coding standards, and delivery practices. Mentor engineers, guide technical roadmaps, and ensure scalable end-to-end deployment across frontend, backend, and ML components.

Lead development and production deployment of machine learning systems for time-series anomaly detection in connected-device environments. Build streaming and MLOps pipelines, tune alerts, monitor model drift, and integrate AI systems with LLM-based assistants. Guide architecture, tooling, observability, and production readiness while mentoring and managing applied AI and full-stack engineers. Collaborate across engineering, product, data, and research teams.

Leads the data science function by managing and coaching data scientists, developing analytics methodologies, partnering with business leaders, and delivering predictive and prescriptive models. Drives adoption of analytical insights, educates stakeholders, manages vendors, recruits talent, and oversees team performance. Requires advanced expertise in statistics, machine learning, Python, R, SQL, Databricks, distributed computing, and cloud analytics.

Lead Data Scientist partners with business leaders to identify opportunities, design, develop, and deploy advanced analytics and ML solutions. Responsibilities include data preparation, modeling, feature engineering, delivering insights and visualizations, establishing standards, and directing associate data scientists, contractors, and consultants to produce measurable business value.

Lead and coach a data science team to design, develop, and deploy predictive and prescriptive analytics for business impact. Partner with senior leaders to identify opportunities, drive adoption of models, manage vendors, and establish data science methodology and best practices.