Top AI & Machine Learning Jobs

Own the platform for deploying, serving, monitoring, and retraining fraud, risk, and payments models in production. Build real-time inference systems, reproducible feature and ML pipelines, model rollout workflows, drift monitoring, third-party model integrations, and merchant-facing risk tooling. Ensure low latency, reliability, explainability, training-serving consistency, and safe model operations for payment authorization systems.

Design and architect secure, scalable healthcare data systems; create core data models using TUVA, OMOP, and FHIR; build production data pipelines and EMR integrations; establish data quality and governance; optimize performance; deploy cloud infrastructure; and ensure HIPAA and HITRUST compliance. The role requires expert SQL and Python or Scala skills, advanced data warehouse and big-data experience, and at least eight years in data science or data engineering.

Design, build, deploy, and scale production-grade AI agents and intelligent systems. Lead technical workstreams, develop agentic workflows, establish evaluation and monitoring practices, make engineering trade-offs, and partner with product and business stakeholders. Mentor engineers, conduct reviews, improve system reliability, evaluate emerging AI technologies, and transition successful solutions to long-term technology teams.

Leads enterprise AI initiatives, technology programs, software development, Azure cloud operations, data warehouse architecture, integrations, and analytics enablement. Oversees .NET and Azure applications, ERP/CRM/payroll integrations, dimensional modeling, ETL pipelines, cloud governance, AI solution delivery, NetSuite optimization, Power BI adoption, and cross-functional performance improvement efforts.

Leads the architecture and strategic direction of generative AI platforms and software solutions. Designs AI agent reasoning, retrieval, enterprise integrations, authentication, testing, observability, safety guardrails, and production deployment practices. Provides technical leadership, reduces technical debt, supports automated workflows, and partners with business and executive stakeholders to deliver scalable, secure AI use cases.

Designs and implements secure AI integrations, autonomous agent workflows, and cloud-based automation connecting diagnostic systems with emerging AI capabilities. The role uses Python, C#, Azure, APIs, and LLM platforms while applying model monitoring, prompt versioning, auditability, NIST-aligned security, HIPAA, and CLIA compliance. Responsibilities also include prototyping AI solutions, migrating offshore knowledge, documenting architecture, troubleshooting interconnected systems, and collaborating with bioinformatics, IT, and software engineering teams.

Leads enterprise AI strategy, consulting, solution design, development, integration, automation, governance, and adoption. Partners with business and technology leaders to identify opportunities, build agentic AI and generative AI solutions, integrate AI with enterprise systems, optimize workflows, and establish secure, scalable, responsible AI practices. The role includes technical leadership, discovery workshops, process assessments, stakeholder advising, platform evaluation, and oversight of AI initiatives across the organization.

Leads the design, development, deployment, and architecture of scalable AI-powered applications within the Alli platform. Manages and mentors engineers, drives coding and testing best practices, conducts code reviews, and collaborates with product and client teams. The role requires hands-on software development across frontend and backend technologies, cloud deployment, generative AI and LLM integration, system architecture, CI/CD, and containerization while providing technical guidance and strategic insights.

Supports data projects across digital marketing, products, business analytics, and student lending. Uses SQL and Python to prepare and analyze data, develop pipelines and dashboards, contribute to predictive models and AI enablement, investigate data quality, document findings, and communicate results to nontechnical stakeholders. This is a 12-week hybrid internship based in Newark, Delaware, or Arlington, Virginia.

Lead the design, optimization, and production deployment of high-performance LLM inference systems using vLLM, CUDA, Triton, C++, and Python. Improve GPU utilization, latency, throughput, scalability, and cost through kernel optimization, quantization, batching, memory management, distributed inference, and profiling. Contribute to upstream open-source projects, establish engineering standards, review performance-critical code, and guide research-driven serving improvements.