Top Artificial Intelligence Jobs

Designs, researches, trains, evaluates, and deploys advanced machine learning systems. Bridges AI research with production engineering by developing scalable pipelines, applying modern deep learning techniques, evaluating research literature, and shipping reliable applied AI solutions. Responsibilities include distributed and mixed-precision training, model fine-tuning, observability, safeguards, and continuous improvement across the ML lifecycle.

Designs, optimizes, and deploys machine learning models for resource-constrained mobile, embedded, and edge devices. Responsibilities include model compression, quantization, pruning, hardware-aware optimization, production deployment, performance profiling, and systems engineering. The role also addresses on-device privacy and security and requires collaboration across teams. Preferred experience includes NPU/DSP toolchains, federated learning, on-device personalization, safety-critical deployments, open-source contributions, and on-device LLM optimization.

Build and operate large-scale data infrastructure for AI training and evaluation pipelines. Responsibilities include data ingestion, transformation, quality assurance, lineage, versioning, reproducibility, and high-throughput delivery across modalities. The role requires operating petabyte-scale storage and pipeline systems, applying distributed systems and data modeling expertise, supporting accelerator-based training, and following strong software engineering practices such as testing, CI/CD, and code review. Collaboration with cross-functional teams is also essential.

Manage and execute AI animation projects, developing innovative workflows, collaborating with teams, ensuring quality storytelling, and tracking production progress.

Support end-to-end evaluation and piloting of legal-technology and AI products: run market research, prepare evaluation criteria and scorecards, coordinate stakeholders (security, procurement, IT, practice), administer pilots, analyze feedback and usage data, produce recommendations and reporting, maintain documentation, and support implementation and change management.

Coach, guide, and facilitate the Enterprise Data and AI program across multiple teams and business partners. Lead Agile program delivery, coordinate stakeholders, drive program outcomes, and maintain accurate documentation and communication using collaboration tools like JIRA and Confluence.

Own concurrent healthcare customer implementations from contract handoff through stabilization. Manage stakeholders, phase gates, status reporting, discovery, workflow mapping, UAT, training, defect triage, and go-live reviews. Diagnose root causes using data and translate operational findings into AI agent configurations, routing rules, extraction fields, and escalation paths. The role combines project management, process design, customer partnership, and structured problem solving without production coding.

Build and deploy production-grade AI agents and automated workflows across manufacturing, supply chain, and business operations. Responsibilities include orchestrating multi-agent systems, integrating enterprise tools and data, implementing RAG pipelines, applying prompt engineering and tool calling, and using MLOps/LLMOps for monitoring and improvement. The engineer will also establish responsible-AI guardrails and collaborate with data science and cross-functional teams to identify automation opportunities.

Supports analytics projects by understanding datasets, developing models, visualizations, reports, and actionable business insights. Collaborates with analytics teams, applies analytical tools to large datasets, adapts to changing business needs, and develops technical and leadership skills through a 12-week summer internship.

Lead end-to-end AI sector diligence: define key questions, design research, conduct expert interviews, synthesize findings to inform investment decisions, build methodology, and manage a 3–5+ junior-person pod at a founding-stage venture intelligence firm.

Lead design and implementation of security strategies for cloud-based AI systems. Conduct risk assessments and vulnerability analyses, enforce policies and compliance, monitor and respond to incidents, integrate security controls into AI workflows, lead audits, recommend security tools, and train teams on AI cloud security best practices while reporting to senior leadership.

Build and maintain end-to-end Microsoft Fabric data engineering and analytics solutions, including lakehouses, warehouses, ELT pipelines, semantic models, Power BI dashboards, and Fabric apps. Develop AI-ready semantic models for Claude, automate audit and data analytics processes, and pilot agentic AI frameworks. Partner with audit stakeholders to translate business needs into governed solutions, lead technical design and standards, manage delivery, and improve adoption, performance, and reliability.

Build and maintain automated testing frameworks for distributed AI training and inference workloads. Develop end-to-end integration and stress-testing plans, validate GPU and hardware-software interactions, and troubleshoot failures in large-scale distributed clusters. Collaborate with systems engineers using telemetry and profiling tools to identify root causes and performance bottlenecks. The role requires Python automation, test framework development, containerization, and CI/CD experience, with Kubernetes, infrastructure automation, networking, and firmware testing experience preferred.

Develop, optimize, package, validate, and deploy AI/ML applications in secure, mission-critical environments. Collaborate with analysts and platform, infrastructure, and security teams; support MLOps, CI/CD, distributed systems, troubleshooting, performance tuning, accreditation, compliance, audits, and technical documentation. The role uses machine learning frameworks including TensorFlow, PyTorch, Scikit-learn, Keras, computer vision, and GANs.

Leads development and deployment of computer vision and AI/ML systems for real-time object tracking, monitoring, and intelligent data triage. Designs scalable architectures, agentic data-collection workflows, and production MLOps pipelines; mentors engineers, oversees data lifecycle and model deployment, and translates client needs into AI/ML roadmaps. Requires advanced Python, computer vision, Docker, Kubernetes, seven-plus years of machine learning experience, U.S. citizenship, and an active Secret clearance or higher.

Design and build production agentic AI systems, including LangGraph orchestration, agent integration contracts, multi-turn context management, clinical and PHI guardrails, RAG pipelines using Azure AI Search, and versioned prompt architectures. Develop evaluation systems, improve retrieval quality, maintain existing applications, and collaborate with cross-functional teams to deliver safe, reliable healthcare technology solutions.

Build Python services, APIs, and data pipelines for semiconductor manufacturing data. Develop and deploy AI/ML models and LLM solutions to automate analysis and generate actionable insights. Design data infrastructure including databases, messaging systems, and web platforms; collaborate with test, yield, and process engineers; identify automation opportunities; and maintain technical documentation.

Supports advisory and implementation engagements involving AI, data, and technology. Responsibilities include building solutions, assessing technology environments, analyzing data, developing recommendations and implementation roadmaps, supporting proposals and client presentations, and contributing to business cases, process improvements, financial models, and technology transformations. The intern collaborates with clients and teams while developing technical, analytical, consulting, and client-service skills.

Supports AI, data, and technology consulting engagements through solution development, client technology assessments, data analysis, process improvement, financial modeling, proposal preparation, and implementation planning. Collaborates with clients and consulting teams to develop recommendations, business cases, technology roadmaps, and presentations while building technical and client-service skills.

Lead customer engagements to qualify and close cloud and AI opportunities on Azure. Drive sales strategy, pipeline hygiene, partner co-sell, and AI-driven forecasting. Collaborate across account teams, enable customer adoption and consumption, and act as trusted advisor to accelerate AI and cloud transformations.

Leads Vertex’s enterprise strategy for digital discoverability across search engines and AI-powered answer platforms. Establishes AEO, GEO, AI-ready website, metadata, schema, taxonomy, structured content, and governance standards. Partners with digital, commercial, medical, analytics, legal, privacy, agency, and technology teams to improve retrieval, indexing, citation, visibility, and measurement. Evaluates emerging AI search and conversational technologies, translates insights into recommendations, and drives organizational adoption in a complex, regulated environment.

Lead AI silicon architecture direction: define specifications and develop functional/analytical models for AI core compute. Collaborate with software and hardware teams, and guide design, DV, emulation, firmware, and kernel teams through end-to-end silicon development and validation.

Serve as a strategic and operational force multiplier for the Edge CVP and leadership team. Translate priorities into execution plans, operating rhythms, decision forums, and accountability mechanisms. Lead talent planning, organizational health, executive communications, special projects, and cross-functional initiatives. Own OPEX, COGS, headcount planning, forecasting, investment tradeoffs, and spend tracking. Manage and develop business management and program management teams.

Develop and support unattended RPA robots in an enterprise environment using UiPath, Office 365, APIs, AI Fabric, Document Understanding, and OCR. Responsibilities include technical design, workflow automation, robot monitoring, operational support, customer collaboration, process analysis, technical documentation, and deadline management. The role also requires strong communication, analytical, and problem-solving skills, with web development experience and UiPath Advanced RPA Developer certification preferred.

Take AI agents from prototypes into secure, reliable production systems. Build, deploy, scale, monitor, govern, and maintain agents; create reusable production patterns; evaluate quality, cost, and usage; implement Responsible AI safeguards; integrate agents with enterprise workflows; and collaborate with data engineering, marketing engineering, and Responsible AI teams.