Top AI & Machine Learning Jobs

Undergraduate intern will develop AI-enabled and digital tools to streamline laboratory visits and tour support. Responsibilities include creating a SharePoint site, secure AI assistant, automated datasets, Power BI dashboards, technical documentation, staff training, testing, feedback collection, and continuous improvement. The role requires collaboration with multidisciplinary teams in a hybrid work environment.

Build and operate governed, production-grade AI and LLM systems across Velsera’s products and business functions. The role covers multi-cloud model serving, MLOps and LLMOps, evaluation, governance, security, auditability, RAG, agentic workflows, and enterprise integrations. This senior individual contributor will establish reusable AI platform patterns, deliver customer-facing and internal capabilities, support regulated environments, and guide build-versus-buy decisions while collaborating with engineering, product, security, compliance, scientific, and commercial teams.

Build AI-powered digital twins that simulate customer behavior and decision-making. Lead data science and AI initiatives involving LLMs, RAG, agentic systems, experimentation, validation, and performance measurement. Apply behavioral science, UX research, and marketing analytics to develop human-centered models. Partner cross-functionally to translate business problems into scalable solutions, communicate recommendations, and mentor junior team members.

Design, develop, and deploy enterprise AI applications using LLMs, RAG, AI agents, vector search, document processing, and cloud-native technologies. Build scalable APIs, integrations, knowledge platforms, and Kubernetes-based services while contributing to architecture, CI/CD, security, testing, and observability. Collaborate with business and technical stakeholders to solve complex problems, evaluate emerging AI technologies, and mentor team members.

Research enterprise AI use cases through interviews with partners, solution architects, and field sales teams. Analyze qualitative and quantitative findings, identify AI adoption opportunities, map customer needs to Red Hat’s AI platform and partner ecosystem, and recommend partner engagement strategies. Present a final report with strategic recommendations to AI Ecosystem leadership.

As a Machine Learning Systems Engineer, you will optimize distributed training systems, enhance performance profiling, and develop high-performance GPUs for machine learning workloads.

Develop advanced data science and AI/ML solutions using complex healthcare data. Apply statistical modeling, machine learning, NLP, and LLM techniques, including RAG and prompt optimization. Build production-ready workflows integrating models with APIs, data pipelines, enterprise applications, and cloud platforms. Analyze data quality and model performance, communicate findings to technical and executive audiences, and collaborate across product, engineering, clinical, technology, and business teams.

Leads a team developing and shipping state-estimation systems for autonomous driving, including multi-object tracking, road-property estimation, and collision-avoidance functionality. Responsibilities include designing classical and machine-learning solutions, analyzing system performance and failures, collaborating with robotics and systems teams, validating solutions, and deploying production-grade perception systems to vehicles.

Leads the account AI strategy, roadmap, use-case portfolio, governance, evaluation, adoption, and performance measurement. Builds and prototypes AI-enabled workflows, automations, agents, retrieval-based solutions, and API integrations; develops testing frameworks, dashboards, documentation, and production handoffs. Partners with technical teams, business leaders, users, and clients to drive responsible adoption, manage risks, communicate value, and connect AI usage to productivity, quality, operational, and commercial outcomes.

Design and implement multi-agent AI systems for marketing compliance workflows. Build production ML pipelines involving model fine-tuning, prompt engineering, retrieval-augmented generation, evaluation, monitoring, explainability, and feedback-driven personalization. Translate compliance rules into agent logic, ensure auditability across customer deployments, and collaborate with engineering, product, implementation, and compliance teams to deliver reliable AI features.