Top Artificial Intelligence Jobs

Advises clients on applying Microsoft 365 Copilot and related AI tools to business processes. Identifies use cases, designs Copilot solutions and agents, leads readiness and envisioning workshops, supports governance and change management, develops training content, and tracks adoption and ROI. Partners with technical teams, sales, and client stakeholders to translate AI capabilities into measurable productivity, cost, risk, and operational benefits.

Lead research and scientific development for fully open foundation models focused on scientific discovery. Build and train modern foundation models, develop training pipelines, curate high-quality data, and investigate agentic and multimodal training. Provide scientific leadership, lead small teams, oversee research software systems, publish and present peer-reviewed work, and build collaborations with academic, industry, government, and standards organizations.

Designs, builds, and operates cloud-native infrastructure for AI and GenAI workloads across Azure and AWS. Responsibilities include GPU and Kubernetes platforms, networking, storage, model serving, RAG infrastructure, observability, CI/CD, infrastructure as code, security, compliance, reliability, and FinOps. The role defines AI infrastructure architecture and standards, leads solution design, operates platforms using SRE practices, creates reusable platform components, and mentors engineers.

Leads the Data and AI architecture strategy, roadmap, governance, operating model, platform standards, vendor strategy, and investment planning for Operations and Finance. Defines scalable, secure, and responsible AI architecture patterns, including data products, MLOps, agentic systems, observability, resiliency, and runtime controls. Develops senior architecture talent and partners with business, technology, security, compliance, enterprise architecture, and vendors to deliver high-value transformation.

Build and deploy reliable AI agents that automate client workflows and integrate with enterprise systems. Responsibilities include mapping processes, designing automation roadmaps, developing evaluation frameworks and regression tests, monitoring agent performance, instrumenting audit trails, scaling systems to production, and leading workforce adoption and change management. The role also codifies reusable patterns across engagements and requires customer-facing collaboration, production AI experience, Python or TypeScript development, Secret clearance, and up to 10% travel.

Design and build resilient data pipelines integrating files, APIs, services, databases, and mission sources. Develop extraction, normalization, enrichment, validation, cataloging, routing, ingestion, and event-driven workflows. Preserve geospatial metadata, provenance, lineage, security attributes, and data quality. Apply AI, RAG, MCP, and agentic capabilities to improve data discovery and access. Build Node.js applications and Oracle Database integrations while collaborating with engineering, cloud, security, AI, and mission teams.

Lead Applied AI product initiatives: identify and co-develop high-impact AI use cases, lead prototypes/MVPs with cross-functional teams, drive adoption of GenAI and AI Enablement capabilities, champion responsible and explainable AI, run AI ideation workshops, manage AI Spoke network, track business value, and own product responsibilities (OKRs, roadmaps, planning, Jira execution).

Design, develop, test, evaluate, and deploy AI/ML models across generative AI, classification, prediction, computer vision, NLP, and reinforcement learning. Provide AI/ML engineering support using algorithms, software development, and cloud computing. The role requires five years of AI/ML development, cloud-based deployment, and Python or R experience, plus U.S. citizenship and an active TS/SCI clearance with polygraph.

Designs, builds, tests, deploys, and scales production-grade AI agents and intelligent systems. Leads technical workstreams, develops agentic workflows, establishes evaluation and monitoring practices, partners with business stakeholders, evaluates emerging AI technologies, mentors engineers, and contributes to reusable engineering patterns. The role requires strong software engineering fundamentals, hands-on AI application development, cloud-native architecture experience, and ownership of complex technical problems through production adoption.

Build and productionize internal AI applications from business-unit prototypes. Responsibilities include auditing AI tools, refactoring applications for security and scalability, integrating LLMs with Salesforce and the enterprise data lake, maintaining deployed solutions, managing stakeholders, and enforcing data governance, access controls, and hallucination mitigation.

Manage a portfolio of B2B SaaS accounts through activation, adoption, and renewal support. Guide product and engineering leaders in using Productboard Spark for AI-first product management, configure AI context, coach prompt usage, deliver activation sprints, monitor adoption metrics, and identify expansion signals. Partner with account executives and renewal managers, document customer insights, improve playbooks, and provide product feedback. The role requires customer-facing technical advisory skills, product management knowledge, and professional AI experience.

Designs, develops, and deploys agentic AI solutions and process automation for Samsung care operations. Responsibilities include multi-agent workflows, MCP servers, enterprise API integrations, databases and vector stores, low-code automation, cloud environments, CI/CD, monitoring, process analysis, and AI governance. The role leads use cases from proof of concept through production, collaborates with business stakeholders and Korea HQ, and communicates technical implementations to nontechnical audiences.

Drives complex, service-led IT solution sales involving managed services, AI, and professional services. Partners with sales teams, executives, solutioning teams, and clients to develop account plans, sales campaigns, proposals, and solution roadmaps. Identifies customer needs, coordinates internal resources and external partners, negotiates commercial terms, manages pipelines and forecasts, and closes opportunities while building trusted relationships with senior customer stakeholders.

Develop and scale AI-powered products, automations, CRM capabilities, and corporate intelligence solutions. Translate business needs into product requirements, manage use cases from ideation through deployment, improve Salesforce and data workflows, create executive insights, validate AI outputs, and support responsible AI governance, user adoption, training, and continuous improvement.

Lead complex enterprise architecture engagements, consumption strategy, and executive technical relationships for Databricks customers. Design scalable data, AI, streaming, migration, and lakehouse solutions; drive platform adoption, competitive displacement, and revenue growth. Orchestrate cross-functional teams, mentor solutions architects, influence senior stakeholders, and contribute to technical thought leadership through workshops, content, and community engagement.

Leads national and locally tailored policy and brand marketing campaigns that build trust in OpenAI. Owns strategy, audience insights, creative development, paid media, stakeholder alignment, execution, optimization, and measurement. Translates complex AI policy and societal topics into accessible narratives for policymakers, civic leaders, communities, and the broader public. Partners cross-functionally with Global Affairs, Communications, Creative, Research, Product, and external agencies.

Drive strategic growth and complex enterprise sales across health plans, PBMs, managed care organizations, and healthcare technology markets. Responsibilities include building executive relationships, developing account and territory plans, managing consultative sales cycles, closing high-value transactions, maintaining forecasts, representing the company at industry events, gathering market intelligence, and coordinating with cross-functional teams on solutions, pricing, contracting, implementation, and post-sale handoffs.

Design, develop, and deploy generative AI, LLM-powered, and agentic systems for federal clients. Lead engineering best practices, implement secure integrations, enable cloud/DevSecOps pipelines, monitor production performance, and mentor engineers while collaborating across data, cloud, and stakeholder teams.

Lead the design, development, deployment, and optimization of scalable AI and machine learning solutions for federal programs. Translate mission requirements into technical architectures, guide cross-functional teams, mentor engineers, evaluate emerging AI technologies, and integrate models into secure cloud production environments. Ensure compliance with federal security, cloud, and architecture standards while overseeing the full AI/ML development lifecycle.

Leads enterprise business intelligence and data analytics for a U.S. Combatant Command program. Develops Power BI architectures, dashboards, data models, pipelines, KPIs, and reporting products for leadership decision-making. Establishes data governance, quality, provenance, metadata, access controls, and authoritative data sources across classified and unclassified environments. Translates stakeholder decision needs into analytic requirements, evaluates knowledge-management initiatives, supports AI and machine-learning capabilities, documents solutions, trains users, and mentors analysts.

Own AI projects from discovery through production, building and deploying AI-powered applications and workflows for GTM, operations, product, strategy, and partner teams. Translate ambiguous processes into scalable systems, integrate applications with the GTM technology stack, gather user requirements, prioritize projects, and coordinate cross-functional execution. Use structured data and context to improve AI reliability and track business performance.

Develop, research, optimize, and deploy computer vision AI/ML algorithms for remote sensing, including automatic target recognition and multimodal EO/SAR data fusion. Responsibilities include processing large datasets, training supervised and unsupervised deep learning models, deploying solutions to cloud environments, reviewing research, documenting capabilities, and potentially leading junior engineers. The role requires an active TS/SCI clearance and supports defense and commercial applications.

Supports the sales process as the primary technical advisor for Infinera’s telecommunications products. Develops and delivers technical presentations, demonstrations, quotations, and RFI/RFP responses; interprets customer requirements; designs and cost-models solutions; supports marketing events and after-sales activities; and builds long-term customer relationships. The role requires expertise in optical, DWDM, long-haul, metro, access, mobile backhaul, and fronthaul systems, along with strong communication and presentation skills.

Supports the development and deployment of production AI systems by fine-tuning and evaluating machine learning models, integrating AI features, working with training and evaluation data, running experiments, monitoring performance, and collaborating with engineering and product teams. Requires current enrollment in a relevant bachelor's or master's program, Python proficiency, familiarity with PyTorch or TensorFlow, and foundational machine learning knowledge.

Define the vision, roadmap, and priorities for AI-powered products. Translate customer needs and ambiguous AI capabilities into product requirements and user experiences, partnering with engineering, research, design, sales, and marketing to launch and improve products. Use customer feedback, usage data, and performance metrics to drive iteration and product value.