Top AI & Machine Learning Jobs

Develop and deploy production-grade AI/ML solutions on Microsoft Azure. Build secure data pipelines, prepare and validate datasets, support NLP and machine learning workloads, and implement MLOps practices including CI/CD, monitoring, troubleshooting, and model lifecycle management. Collaborate with engineers, analysts, and stakeholders in Agile teams while producing architecture, testing, security, and operational documentation for regulated government environments.

Build and deploy AI agents, automations, and tools that transform organizational processes. Embed with teams to map workflows, deliver time-boxed solutions using Claude and related platforms, measure business value, document and hand off production-ready systems, troubleshoot during stabilization, and create reusable patterns. Partner with AI Platform Operations, IT, risk, and staff while ensuring responsible handling of sensitive data and teaching teams to maintain solutions.

Own Alpaca’s growth data strategy across acquisition, activation, retention, referral, and monetization. Build experimentation frameworks, causal and incrementality analyses, funnel and cohort models, LTV/CAC and churn forecasts, marketing measurement, governed growth metrics, and self-serve analytics. Partner with Product, Marketing, Sales, Engineering, and Finance to influence strategy and roadmaps, communicate insights to leadership, mentor data professionals, and establish standards for growth analytics.

Develop AI/ML features using LLMs, generative AI, RAG, conversational agents, and machine learning models. Build production pipelines, integrate model APIs, evaluate quality and performance, and create secure full-stack services. Collaborate with product, architecture, infrastructure, security, and QA teams while documenting solutions and contributing to code reviews.

Build and deploy LLM-powered features for clinical, patient, and operational workflows. Responsibilities include prompt design, retrieval, tool use, agent orchestration, evaluation harnesses, feedback loops, production operations, and AI engineering tooling. Partner with clinical, product, and safety stakeholders to ensure responsible healthcare-grade AI. Mentor teammates, communicate model behavior clearly, and stay current with emerging AI research and development practices.

Build and deploy AI-powered tools, developer workflows, testing pipelines, integrations, and internal interfaces across the organization. Evaluate frontier models and emerging AI tools, improve CI/CD and automated testing, support go-to-market and operations teams, and establish practices for agentic engineering, security, responsible AI, reliability, and cost management. The role requires cross-functional collaboration and rapid prototyping.

Develop and deploy AI and machine learning models for production optimization, quality control, anomaly detection, predictive maintenance, supply chain forecasting, and operational analytics. Build scalable data pipelines, MLOps processes, APIs, dashboards, and LLM-powered solutions using prompt engineering, RAG, fine-tuning, and AI agents. Analyze complex datasets, conduct statistical experiments, monitor model performance, and promote responsible AI practices.

Leads the strategic vision, organizational direction, and delivery of advanced data science programs. Manages multiple teams of data scientists and analysts, including managers, while overseeing statistical modeling, machine learning, data pipelines, and analytical frameworks. Partners with executive stakeholders to align initiatives with enterprise goals, advocates for resources, develops talent, ensures regulatory compliance, and promotes innovation and ethical data practices.

Build and lead production AI systems as Jobber’s founding AI engineer within the Office of the CEO. Responsibilities include architecting internal AI platforms, working with LLMs, retrieval and agents, ensuring trust, permissions, security and reliability, embedding AI into company workflows, evaluating build-versus-buy decisions, running rapid proofs of concept, and setting technical standards. The role partners closely with executives and teams across the company.

Lead the design and production delivery of agentic AI systems, translating domain expertise into ontologies, tool contracts, evaluation frameworks, and reliable orchestration patterns. Own context management, guardrails, tracing, testing, and observability while setting technical direction for an engineering pod. Collaborate with engineers, subject matter experts, product leaders, security teams, executives, and clients. The role requires production experience with agent frameworks, multiple model families, cloud AI platforms, agentic development practices, and coding tools.