Top AI & Machine Learning Jobs

Design and build backend systems for AI agent orchestration, tool execution, workflow management, memory, and state handling. Develop scalable APIs and reliable infrastructure for distributed AI workflows, improve platform reliability and observability, and partner with product, research, and engineering teams to deliver agent capabilities. Own technical initiatives from architecture through production, maintain software quality through testing and continuous delivery, and mentor engineers on backend and distributed-systems practices.

Build and maintain production generative AI agents, LLM services, APIs, RAG pipelines, evaluation frameworks, and observability infrastructure. Develop intent recognition, query decomposition, context engineering, and self-service data capabilities. Integrate agents with internal and third-party systems, optimize latency and cost, automate business processes, and support AI-driven media planning and data monetization. Collaborate cross-functionally in an agile environment while adapting to evolving requirements.

Own and improve 2D, 3D, and fusion-based image segmentation models for clinical ultrasound and CT analysis. Build training, evaluation, dataset, and labeling pipelines; collaborate with clinicians, contractors, and data teams; and productionize auditable, low-latency inference services with confidence scoring, drift monitoring, and safe fallbacks. Work under medical-device design controls and support robust modeling across diverse patient anatomy and imaging conditions.

Leads Lyra Health’s AI/ML engineering roadmap, scales and mentors machine learning teams, and drives production-grade systems for clinical applications. Responsibilities include people leadership, quarterly planning, technical architecture, design reviews, model governance, ML SDLC oversight, and deployment of reliable microservices on Kubernetes. The role partners with product, data science, and clinical stakeholders while ensuring safety, operational excellence, cloud scalability, and compliance for sensitive healthcare data.

Supports AI, automation, and digital transformation initiatives in a manufacturing environment. Responsibilities include documenting processes, analyzing workflows and data, preparing process maps and presentations, assisting with AI and automation pilots, developing training materials, tracking project KPIs, and researching emerging AI tools. The role collaborates with teams across HR, Finance, Supply Chain, Sales, IT, and Marketing.

Build and maintain production LLM-based tools, agent workflows, reporting automation, and AI-driven analysis for enrollment and financial projections. Connect AI systems to Ellucian Colleague and other institutional systems, manage models and integrations, ensure reliability and cost control, and uphold FERPA-compliant data governance. Advise executive leadership on practical AI applications and independently turn business requests into working tools.

Designs and implements production machine-learning workflows for semantic search, classification, entity resolution, retrieval, embeddings, and RAG. Builds reliable AI services, integrates models with data and backend systems, evaluates mission performance, and supports deployment, monitoring, versioning, and MLOps practices. The role also contributes to architecture decisions and may support defense, intelligence, or mission-focused environments.

Build and productionize machine learning, NLP, generative AI, and analytics solutions for healthcare and regulated federal data. Responsibilities include data preparation, feature development, experimentation, model evaluation, error analysis, validation, workflow monitoring, troubleshooting, and translating stakeholder requirements into technical solutions.

Design and deploy AI systems that convert unstructured healthcare, scientific, and financial information into decision-relevant insights. Own applied AI projects from problem definition through deployment, evaluation, and improvement, using NLP, RAG, knowledge graphs, embeddings, entity extraction, and agentic systems. Develop evaluation frameworks, ensure data quality and reproducibility, and collaborate with investment professionals and senior stakeholders to support investment research and decision-making.

Build agentic AI systems for engineering workflows, develop and validate machine learning models, analyze structured and unstructured data, engineer features, and contribute to LLM agent development. Assist with cloud deployment, secure enterprise pipelines, AI governance, and feedback processes while collaborating with product, engineering, and business teams.