Top AI & Machine Learning Jobs

Lead the design, development, and deployment of enterprise Generative AI solutions. Architect LLM, agentic AI, RAG, orchestration, and cloud-native workflows across AWS, GCP, and Snowflake. Establish prompt governance, guardrails, evaluation frameworks, benchmarking, observability, and optimization practices. Partner with stakeholders to prototype AI solutions and convert them into scalable, secure, production-ready applications.

Design, build, deploy, and support enterprise generative AI products using LLMs, RAG, agentic workflows, Python, Azure, and cloud-native technologies. Develop APIs, vector search solutions, evaluation and observability systems, and production services on AKS. Translate business needs into scalable product capabilities while balancing quality, latency, cost, security, and reliability. Collaborate with product, architecture, infrastructure, database, operations, and engineering teams on delivery, troubleshooting, incident response, and ongoing enhancements.

Leads end-to-end data science and AI solutions for client and advisor analytics, including problem framing, feature engineering, predictive modeling, GenAI, validation, deployment, monitoring, and governance. Owns the analytics strategy for a recommendation engine, partners with business, product, and technology teams, communicates actionable insights, advances scalable practices, and mentors peers.

Leads the design, development, and delivery of production AI systems, including agentic workflows, RAG architectures, backend services, evaluations, and orchestration infrastructure. Provides hands-on technical leadership, architectural direction, mentorship, code reviews, and engineering standards improvements. Partners with product and engineering stakeholders to translate ambiguous requirements into reliable, observable, scalable, and maintainable AI solutions.

Design, develop, test, deploy, and maintain enterprise full-stack applications using modern frontend, backend, cloud, database, and integration technologies. Build AI-enabled features with LLM APIs, prompt engineering, RAG, vector databases, and agent frameworks. Implement APIs, microservices, CI/CD pipelines, automated testing, DevOps, secure integrations, and Azure cloud solutions. Provide technical leadership, architecture guidance, mentoring, troubleshooting, documentation, and collaboration with enterprise stakeholders. This role requires 100% onsite work in Harrisburg, Pennsylvania.

Build, retrain, validate, and maintain data-driven models for sports, concerts, and live events. Investigate data quality issues and unexpected model behavior, fulfill recurring and ad hoc modeling requests, and use Python and SQL to manipulate datasets and troubleshoot workflows. The role also supports internal application development, debugging, automation, process improvements, and cross-functional collaboration to deliver accurate and reliable modeling outputs.

Evaluate, configure, integrate, and develop AI and enterprise technology solutions for legal and business operations. Translate stakeholder needs into technical requirements, test applications and AI workflows, build prototypes, automate processes, develop API integrations, troubleshoot data flows, and support implementation. The role requires maintaining reliable, secure, scalable solutions while documenting findings and collaborating with legal, engineering, IT, security, data, and application teams.

Leads the Machine Learning Engineering team supporting League of Legends. Owns ML engineering vision, strategy, MLOps, model lifecycle management, production reliability, experimentation, and measurable player impact. Manages engineers, budgets, recruiting, staffing, mentoring, performance, and organizational planning while partnering with product and technical leaders to deliver scalable ML-powered experiences and tools.

Build and deploy statistical and machine-learning models for forecasting, risk prediction, retention, no-show prediction, experimentation, causal inference, and clinical outcomes. Partner with analytics engineering and technology teams to productionize models using monitored data pipelines. Translate ambiguous clinical and operational questions into rigorous analyses, communicate recommendations to cross-functional stakeholders, and establish data science and responsible AI practices.

Leads strategic adoption of AI for cybersecurity, including identifying use cases, evaluating AI technologies, developing roadmaps, managing cross-functional projects, and ensuring security, privacy, and compliance. The role bridges AI, security operations, threat intelligence, incident response, and executive stakeholders while overseeing pilots, implementation plans, metrics, training, and change management.