Top AI & Machine Learning Jobs

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.

Lead architecture and technical strategy for enterprise AI solutions using Gemini Enterprise and Vertex AI. Drive consultative sales support, design agentic and multi-agent RAG systems, oversee prompt engineering and model tuning, define MLOps and data governance on GCP, and ensure technical quality control during delivery.

Develops end-to-end AI and machine learning solutions for enterprise marketing, including predictive, Bayesian, NLP, deep learning, graph, and reinforcement learning models. Builds automated MLOps pipelines, model-serving APIs, dashboards, and containerized Kubernetes deployments. Oversees model evaluation, monitoring, drift mitigation, and continuous improvement while translating business needs into technical roadmaps. Communicates model performance and recommendations to technical, executive, and business stakeholders.

Architect and build secure, scalable agentic AI systems using LLM frameworks, AWS services, retrieval technologies, and multi-agent orchestration. Develop production-grade infrastructure, tool integrations, monitoring, evaluation pipelines, guardrails, and governance controls. Optimize agent performance, latency, reliability, and cost while collaborating with data science, engineering, security, risk, and compliance teams. Lead full lifecycle delivery from design through deployment and continuous improvement, including cloud-native and on-premises LLM solutions.

Design and build an enterprise AI platform on GCP supporting LLMs, agentic workflows, scalable cloud-native applications, and federated development. Establish standards for AI governance, security, evaluation, observability, and operational excellence. Define microservices, event-driven, API-first, and CI/CD patterns; guide engineering teams through architecture reviews and hands-on implementation; and enable reliable integration with legacy systems.

Develop and deploy AI, machine learning, statistical, and adaptive process-control models for bioprocess optimization. Analyze sensor, batch, and experimental data; improve fermentation and downstream processing productivity, yield, cost, and scalability. Collaborate with engineers and scientists to integrate data science solutions into manufacturing and R&D operations, enhance data pipelines and modeling frameworks, and communicate findings to technical and non-technical stakeholders.

Lead the enterprise AI platform and agent ecosystem, including multi-agent orchestration, reusable agent templates, self-service RAG, tool and skill registries, prompt management, evaluation frameworks, guardrails, and human-in-the-loop workflows. Partner with cybersecurity, architecture, governance, and business teams to deliver secure, scalable, compliant AI solutions. Drive adoption through low-code tooling, mentor engineers, and shape enterprise AI strategy across commercial, medical, R&D, and corporate functions.