Top Artificial Intelligence Jobs

The consultant identifies and prioritizes enterprise AI opportunities, translates business challenges into AI use cases, designs and prototypes AI-powered workflows, and supports scaled implementation. Responsibilities include coordinating cross-functional delivery, establishing governance and operating models, measuring adoption and business impact, optimizing prompts and workflows, and enabling organizational change through training and stakeholder engagement. The role requires strong consulting, generative AI, communication, and solution-oriented skills.

Develop and operationalize AI-native engineering tools, workflows, standards, and reference architectures across the enterprise. Research emerging technologies, build proofs of concept, integrate approved tools, and create scalable engineering patterns. Develop and deliver AI training, tutorials, documentation, presentations, and demonstrations for technical teams and executives. Support adoption through office hours, change management, stakeholder communication, troubleshooting, and feedback gathering.

Lead technical strategy and solution execution with strategic ASEAN partners across Microsoft Cloud and AI. Guide partners in architecture, offer development, proof-of-concepts, innovation workshops, technical enablement, and co-sell execution. Help create scalable Copilot, Power Platform, business automation, and agentic AI solutions that can be packaged, sold, and delivered repeatedly. Build relationships with partner executives, activate specializations, resolve technical blockers, and translate technical capability into measurable revenue and customer outcomes.

Leads enterprise application and data architecture initiatives across PLM and MDM platforms. Defines technical roadmaps, designs service-oriented and reusable software components, evaluates emerging technologies, and ensures data quality across integrated systems. Partners with business leaders, manages vendors, develops business cases, mentors technical teams, and builds proof-of-concept solutions using cloud, DevOps, microservices, big data, and enterprise search technologies.

Execute data mapping, interfaces, integrations, and data-quality tasks across PLM and MDM platforms. Develop, configure, test, and document reusable service-oriented software and data-delivery components. Build proof-of-concepts, support enterprise architecture standards, evaluate tools, collaborate with business and project teams, and address product-structure and systems-integration challenges. The role requires experience with application engineering and exposure to ALM, PLM, microservices, big data, enterprise search, cloud, and DevOps technologies.

Leads global procurement strategy for AI, software, and emerging technologies. Responsibilities include strategic sourcing, supplier evaluation, complex contract negotiations, category strategy, supplier relationship management, risk mitigation, ESG integration, and pricing analysis. The role also develops procurement playbooks, delivers training, aligns cross-functional stakeholders, assesses technology use cases, and leads strategic initiatives while influencing senior leaders across a matrixed organization.

Leads complex satellite assembly, integration, and test campaigns, including test strategy development, execution, troubleshooting, reporting, and systems compliance. Manages AI&T budgets, schedules, resources, and program plans while partnering with program and functional leaders. Drives continuous improvement, mentors engineers, leads cross-functional teams, and communicates technical information to stakeholders. The role requires advanced test engineering, systems engineering, automated testing, environmental testing, and technical leadership experience.

Own execution of Zynga’s internal AI operating platform from intake and triage through release and post-launch measurement. Partner with engineering on complex platform capabilities including chat, knowledge bases, connectors, workflows, and documents. Define requirements, conduct technical research, establish metrics, prioritize requests, unblock delivery, and drive operational rigor across multi-team workstreams.

Leads enterprise architecture, design, governance, and adoption of AI, machine learning, generative AI, analytics, RPA, intelligent automation, and data platform solutions. Translates business strategy into scalable, secure, compliant architectures across Azure, AWS, SaaS, and Microsoft technologies. Partners with business, engineering, security, governance, and operations teams; establishes reusable standards and patterns; guides delivery from concept through production; leads architecture reviews, prototyping, performance improvements, compliance, and technical mentoring.

Support clients in identifying, designing, prototyping, and scaling generative AI, automation, and agent-based workflow solutions. Translate business challenges into practical use cases, define success metrics, coordinate cross-functional delivery, evaluate AI tools, establish governance, measure impact, optimize workflows and prompts, and support training, change management, documentation, and responsible AI adoption.

Build and scale AI infrastructure through Python automation, AWS serverless architectures, EKS containerized workloads, Terraform infrastructure as code, and monitoring. Develop orchestration pipelines for model training and inference, manage large datasets and production availability, reduce recovery times, collaborate with QA and Product, and mentor engineers while maintaining code quality and operational performance.

Build and operate infrastructure for evaluating coding agents and language models. Design end-to-end features spanning data models, APIs, background workers, and UI; run sandboxed trials; score and aggregate results; improve benchmark quality checks; integrate models and agents; and maintain performance, reliability, concurrency, migrations, tests, and code quality.

Own the development and operation of AI-powered accounting workflows for reconciliations, utility billing, cash management, month-end close, and financial reporting. Use coding agents, APIs, and the proprietary Maynard platform to automate processes, monitor exceptions, improve accuracy, and reduce manual work. Collaborate with engineering and leadership, investigate accounting issues, and help offshore accounting teams adopt AI tools.

Designs governed AI architectures, solution patterns, and reference designs for a mission-critical defense and intelligence enterprise. Responsibilities include architecting multistep agentic AI workflows with retrieval, tool calling, and automated reasoning loops; integrating solutions across Azure, AWS, and GCP; and monitoring system health, performance, and availability. The role requires extensive enterprise Solutions Architect experience, secure scalable integration expertise, an active Top Secret clearance, and willingness to obtain a polygraph.

Lead end-to-end design, development, and delivery of enterprise Generative AI and ML solutions. Architect scalable LLM, RAG, and agent-based systems, drive technical decisions, implement production-ready MLOps/CI-CD, and mentor engineers. Collaborate with product and business stakeholders, ensure monitoring, security, and operational support, and evaluate emerging AI technologies to improve delivery.

Owns post-deployment value, adoption, and expansion for strategic Private Cloud AI customers across North America. Provides enterprise AI architecture guidance, identifies GenAI and agentic AI use cases, leads technical and business reviews, develops expansion cases, and partners with Sales, Product, Engineering, and Support. Builds reusable workshops, reference architectures, metrics, and operating processes for a global AI value motion. The role is customer-facing, commercially oriented, hybrid, and requires frequent North American travel.

Leads predictive modeling, causal inference, marketing-mix modeling, patient identification, HCP targeting, segmentation, and next-best-action analytics for pharmaceutical commercial decisions. Establishes model governance, validation, monitoring, and lifecycle standards while enabling AI-supported analytics and governed self-service data access. Works hands-on with Python, SQL, cloud platforms, and notebook environments, partnering with brand, omnichannel, engineering, and commercial teams to activate and measure analytical outputs.

Leads SEO strategy across client accounts, managing technical SEO, content optimization, AI Search visibility, audits, measurement frameworks, reporting, recommendations, and delivery quality. Builds client relationships, translates search data into business decisions, mentors junior consultants, supports pitches and account growth, and improves internal SEO processes. Requires strong technical SEO, analytics, client advisory, project ownership, and communication skills, with working knowledge of AI Overviews and LLM-powered discovery.

Leads strategy and product roadmaps for AI-enabled agriculture programs serving smallholder farmers in low- and middle-income countries. Responsibilities include testing delivery channels, scaling innovations, overseeing forecast development, conducting message experiments, managing partnerships, supporting cost-effectiveness and monitoring, developing toolkits, and leading staff. The role requires scaling international development programs, collaborating with governments and researchers, and traveling internationally approximately 10–20%.

Own the rollout and scale of AI agents and shared AI platform capabilities across Teamworks’ sports technology products. Drive beta-to-general-availability launches, AI-powered workflows, technical standards, integrations, evaluation datasets, regression testing, permissions, usage measurement, packaging, and customer adoption. Partner across product, engineering, and commercial teams to deliver reliable, secure AI experiences handling sensitive athlete data.

Own the official costed BOM, system and cluster cost baselines, and platform TCO models for AI infrastructure. Analyze component, manufacturing, deployment, site, service, and lifecycle costs; assess engineering changes and sourcing alternatives; identify and validate cost-reduction opportunities; reconcile actuals and forecasts; manage cost-data standards and workflows; and present scenario-based recommendations to engineering, sourcing, manufacturing, finance, planning, and operations leaders.

Lead research advancing foundation models, formal reasoning, verification, reinforcement learning, synthetic data, and agentic AI. Develop and evaluate training and post-training methods, benchmarks, and structured representations for reliable reasoning. Own end-to-end research initiatives, guide researchers and engineers, and translate breakthroughs into production capabilities. The role requires strong expertise in modern AI research, rigorous experimentation, Python and PyTorch or JAX, plus technical leadership and a record of impactful deployed research.

Leads advanced testing and validation of aerospace systems and components, including test planning, procedures, reporting, equipment use, and technical problem solving. Mentors early-career engineers, oversees program milestones, represents Test Engineering with senior stakeholders, and serves as a prime technical contact on major programs. Requires expertise in test engineering methods and active Top Secret clearance.

Leads the design, operation, optimization, and governance of cloud, data, and AI platforms. Responsibilities include performance monitoring, incident response, disaster recovery, infrastructure design, automation, CI/CD, Azure DevOps workflows, and Azure Machine Learning and AI Foundry deployment pipelines. The role also establishes operational standards, improves observability, manages complex troubleshooting, supports security controls, documents platform processes, and mentors junior engineers.

Lead SEO strategy for client accounts, owning technical SEO and AI discovery audits, measurement frameworks, recommendations, delivery quality, client relationships, mentoring, and account growth. Analyze data from search, analytics, crawling, ranking, log, and AI visibility tools. Guide technical, content, architecture, and visibility initiatives while managing workstreams, timelines, stakeholder communication, and practice improvements.