Top AI & Machine Learning Jobs

Develop and maintain credit risk models for application and behavioral scoring, default and loss forecasting, fraud detection, underwriting, collections, and early risk detection. Create features from alternative financial data, evaluate vendors, productionize models with engineering and product teams, and ensure compliance with fair lending standards. Monitor, validate, recalibrate, and back-test models while communicating insights to technical and non-technical stakeholders.

Leads the strategy, architecture, delivery, and operationalization of agentic AI solutions in enterprise healthcare environments. Manages engineering teams building LLM workflows, copilots, intelligent automation, and integrated AI platforms. Responsibilities include cloud architecture, secure deployment, AI evaluation, stakeholder alignment, delivery planning, technical governance, executive communication, and coaching engineering leaders. The role also drives scalable engineering practices and production-ready solutions across marketing technology deployments.

Design, build, and deploy scalable AI/ML platforms and AI-powered solutions using patient data. Collaborate with product managers, data scientists, engineers, and stakeholders to deliver reliable systems through CI/CD, testing, cloud infrastructure, and quality gates. Contribute to architecture and technical strategy, evaluate emerging generative and agentic AI technologies, and promote responsible, maintainable, data-driven AI innovation.

Leads a team of AI engineers while designing, deploying, monitoring, and optimizing agentic AI systems and AI-driven developer lifecycle tools. Oversees production delivery, team development, PI planning, resource forecasting, roadmaps, stakeholder collaboration, and healthcare provider platform enhancements. The role requires hands-on expertise with agent orchestration, LLM platforms, observability, performance tuning, and AI-enabled SDLC automation, along with strong leadership and delivery management.

Leads enterprise AI/ML and mathematical optimization initiatives across healthcare, including solution architecture, hybrid ML-optimization modeling, generative and quantum-inspired AI, solver integration, MLOps, governance, and production deployment. Develops prototypes and reusable frameworks, guides technology adoption, mentors engineers, leads small teams, and partners with business, product, architecture, and engineering stakeholders to deliver scalable, compliant optimization solutions.

Leads AI-first platform and full-stack engineering teams, setting technical direction, architecture, governance, and delivery strategy. Oversees scalable applications, APIs, microservices, data layers, AI/ML model governance, responsible AI, CI/CD, and compliance practices. Builds and develops engineering organizations, manages roadmaps and cross-team dependencies, drives productivity through automation and AI-assisted development, and partners with executive, product, architecture, data science, and business stakeholders.

Leads enterprise AI/ML and Generative AI engineering strategy, architecture, delivery, MLOps, governance, and production deployment. Builds and mentors engineering teams, drives LLM, AI agent, predictive analytics, and automation initiatives, evaluates emerging technologies, and partners with business, product, architecture, and technology stakeholders to deliver secure, responsible AI solutions in a healthcare technology environment.

Leads enterprise Data and AI platform strategy, data engineering, AI/ML operationalization, governance, modernization, and cloud cost optimization. Oversees data products, pipelines, APIs, real-time processing, MLOps, GenAI, Agentic AI, vector databases, RAG, monitoring, and Responsible AI. Partners with executives and technical stakeholders to prioritize platform investments, establish performance metrics, manage budgets and vendors, and build high-performing engineering teams.

Serves as a client-facing data science and technology consultant for a healthcare data platform. Analyzes large-scale claims, clinical, and unstructured datasets; develops statistical, machine learning, predictive, and prescriptive models; and creates prototypes and scalable analytics solutions. Partners with product, engineering, and client services teams, translates client needs into recommendations, supports onboarding and troubleshooting, and provides technical guidance on AI, cloud analytics, and platform adoption.

Leads the design, development, deployment, and monitoring of scalable production AI/ML solutions. Responsibilities include data preparation, feature engineering, model development and optimization, ML pipeline implementation, drift analysis, observability, responsible AI, and multi-cloud infrastructure. The role collaborates with engineering, product, data science, and UX teams to deliver technical solutions and integrate batch and real-time data pipelines.