Top AI & Machine Learning Jobs

Build and deploy AI systems for supply chain optimization, including LLM-powered Text-to-SQL and RAG applications, proactive anomaly alerts, and autonomous agents. Develop machine learning models, data pipelines, and secure human-in-the-loop workflows. Collaborate with engineers, data scientists, analysts, and supply chain stakeholders to translate logistical challenges into production-ready AI solutions.

The AI Engineer will develop AI/ML solutions for investment analysis, design applications, optimize models, and work with large datasets in healthcare and finance contexts.

Tune AML and fraud detection models using customers’ live transaction data. Investigate missed cases, alert quality, pattern drift, and false positives; validate changes through backtesting and sample review; document regulatory rationale; and present model performance and business value to compliance, risk, and data science stakeholders. Partner with customer teams on account strategy and relay customer-specific or systemic findings to product and model functions.

Lead AI engineering teams in designing, developing, deploying, and monitoring scalable AI solutions. Integrate machine learning, NLP, generative AI, and prompt engineering into products while ensuring performance, security, fairness, compliance, and reliability. Mentor engineers, establish testing and production metrics, manage model lifecycles, promote CI/CD and TDD practices, and collaborate with architects, data teams, product managers, and stakeholders to deliver responsible AI-powered products.

Collaborate with stakeholders to analyze business requirements and identify opportunities for digital and AI solutions. Assist with designing, developing, and implementing AI models, algorithms, and digital applications. Research emerging AI, machine learning, and data science trends, gather feedback, integrate solutions into existing systems, and support continuous improvement and innovation.

Lead complex data science projects using statistical analysis, predictive modeling, machine learning, data mining, and visualization. Develop models and algorithms, assess data quality and model performance, communicate actionable findings to technical and executive stakeholders, and provide mentorship to other data scientists. Partner with multidisciplinary teams to improve operations and manage multiple priorities.

Supports Naval Surface Warfare Center mission engineering by applying data science, statistics, machine learning, data mining, graph algorithms, and operations research to electromagnetic spectrum data. Builds and maintains data pipelines and analytical systems, performs statistical investigations, supports software integration and testing, develops scripts and APIs, and converts large datasets into decision-quality insights.

Build and maintain production internal tools, web applications, integrations, AI agents, MCP servers, and automation systems across Finance, Accounting, Legal, People, Recruiting, and Sales. Own projects end to end, automate billing and revenue processes, integrate internal systems with ERP and Salesforce, enable business users to create automations, and measure business impact.

Define Cisco’s multi-year AI research strategy and architecture for purpose-built large language models in networking, security, data center, and observability. Lead massive-scale pre-training, continual pre-training, reinforcement learning, alignment, agentic framework, and autonomous operations initiatives. Advise executive leadership on production-scale AI ecosystems, publish influential research, lead academic initiatives, and mentor senior research talent.

Design, develop, validate, fine-tune, and deploy AI/ML models (including LLMs and SLMs) and AI agents. Improve model performance using transformers, distillation, and reinforcement learning. Collaborate cross-functionally to build scalable, responsible solutions for delivery intelligence, network automation, edge computing, and DevOps automation.

Leads the development, operationalization, deployment, monitoring, and scaling of machine learning models and software components. Partners with data scientists, engineers, analysts, and product teams to solve complex business problems. Builds data pipelines, automates testing and CI/CD, applies cloud architectures, manages model performance and drift, and coaches team members while promoting innovation and agile development.