Top Artificial Intelligence Jobs

Lead and develop a team of Cloud Solution Architects for Telco, Media & Gaming customers, driving Azure and AI adoption. Provide technical leadership across cloud, data, and AI engineering (architecture reviews, DevOps/MLOps, responsible AI, security), manage multiple engagements, ensure delivery predictability, and align technical outcomes to customer business objectives while collaborating across sales, product, and partner teams.

Lead Azure sales and pre-sales strategy for strategic Oil & Energy accounts, driving pipeline, consumption growth, and C-level relationships. Translate industry insights into cloud and AI solutions, orchestrate partners and internal teams, lead POCs/ pilots, mentor junior staff, and promote Azure AI/analytics modernization to deliver measurable business value.

Leads customer-facing cloud architecture engagements across Azure AI, applications, infrastructure, and data workloads. Advises executives, designs secure and scalable solutions, guides implementation and operational excellence, resolves technical blockers, oversees strategic account delivery, and accelerates Azure adoption. The role also includes mentoring peers, contributing technical thought leadership, supporting proposals, maintaining cloud certifications, and helping enterprise customers adopt AI and modernize applications at scale.

Designs and delivers secure, scalable Azure infrastructure and cloud transformation solutions for customers. Leads architecture sessions, migrations, modernization, proofs of concept, and production deployments across infrastructure, applications, data, SAP, hybrid environments, and AI workloads. Partners with technical and sales teams to identify opportunities, resolve blockers, optimize customer environments, develop reusable intellectual property, and drive adoption, resiliency, performance, and business value.

Lead research and development of advanced AI solutions (LLMs, generative and agentic systems). Prototype, evaluate, and productionize models; set architecture and governance standards; mentor teams; publish research and drive enterprise adoption within a regulated financial services environment.

Lead technical pre-sales engagements for Microsoft 365 Copilot, Copilot Studio, AI agents, and cloud endpoint solutions. Deliver demonstrations, discovery sessions, whiteboarding, solution designs, and proof-of-concept support. Develop technical win strategies, address objections and blockers, collaborate with partners and internal teams, educate customers, and guide adoption while embedding governance, security, and compliance into proposed solutions.

Senior individual contributor leading outreach, community strategy, and brand for the AI Center of Excellence. Designs flagship programs, coordinates cross-functional and executive-facing events, owns editorial voice and measurement, and partners with GL&D, Communications, Technology, Risk, and external partners to drive AI upskilling, engagement, and program impact across the enterprise.

Design scalable enterprise AI/ML solutions using Snowflake and partner technologies, conduct root-cause and predictive failure analysis, manage data governance and security, and align technical roadmaps with customer ROI. The role mentors architects on MLOps and architectural design, communicates with technical and executive audiences, and supports robust deployments across distributed cloud systems. Responsibilities span the full data science lifecycle, including feature engineering, model development, deployment, and management.

Serve as the primary engagement lead for business teams adopting AI: shape use cases, manage intake readiness, orchestrate triage and decisioning, coordinate stakeholders, and drive post-launch adoption and value realization. Act as connector across Product, Enablement, Strategy, and business teams to ensure sustained AI adoption and continuous improvement.

The Staff AI Engineer will analyze AI quality, implement improvements, manage projects, collaborate with teams, and shape AI capabilities in a remote-first environment.

Build and scale AI platforms, including autonomous multi-agent systems and cognitive architectures. Develop asynchronous Python microservices and REST/gRPC APIs, establish AIOps and MLOps CI/CD pipelines, automate AI model testing and monitoring, and architect scalable data pipelines across Snowflake, Databricks, and Lakehouse environments.

The Senior Product Manager will prioritize and deliver AI products, support cross-functional teams, enhance key metrics, and drive market leadership through innovation and research.

Leads supply chain data science and AI operationalization for Global Service Operations. Converts manual analytics into governed, automated AI-assisted workflows, taking solutions from prototypes to production with lineage, monitoring, lifecycle management, and ownership. Manages analysts, delivers against SLAs, develops statistical and machine-learning models, and communicates actionable insights across procurement, fulfillment, reverse logistics, inventory, and supplier operations. Measures cycle time, quality, and business impact while mentoring the team and advancing enterprise AI governance.

Lead post-go-live optimization for Microsoft Dynamics 365 Supply Chain Management environments. Assess configuration and adoption across inventory, procurement, warehousing, planning, and related modules; identify business pain points; create enhancement roadmaps; guide system configuration changes; and apply AI and Copilot automation opportunities. The role also includes client and executive stakeholder engagement, relationship expansion, methodology development, mentoring, backlog management, and practice growth.

Work across AI, backend, and frontend: curate and clean writing datasets, fine-tune large language models, run training experiments, develop metrics and eval sets, build backend services in Python/Django, and implement frontend features with React.

Lead and coach AI engineering teams to partner with business stakeholders, scope and deliver production-ready LLM/agent/coplilot solutions. Define safe experimentation, governance, evaluation, observability, and reusable assets while coordinating risk, compliance, security, and operations in a regulated banking environment.

Build and scale LLM-based, agentic customer-engagement systems: architect, deploy, and monitor production ML features; finetune models; create evaluation frameworks, feedback loops and HITL workflows; run A/B tests; optimize latency, cost, and reliability; and mentor the team.

Leads enterprise AI architecture and technical vision across multiple products. Designs RAG and agent workflows, integrates and optimizes LLMs, and builds secure, observable, high-availability, multi-tenant systems. Establishes AI security, monitoring, and cost-management standards while partnering with product, data science, and engineering leaders. Mentors technical teams, drives cross-functional initiatives, and guides technology roadmaps. The role requires advanced cloud, containerization, vector database, embedding optimization, and production-scale AI application experience.

Develops and refines LLM prompts, translates business needs into AI solutions, and collaborates with software developers to integrate LLM capabilities into applications. The role contributes to scalable prompt-engineering frameworks, analyzes user feedback, documents best practices, evaluates emerging LLM technologies, and supports platform-selection decisions. It requires advanced technical expertise, strong communication, and collaboration with domain and nontechnical stakeholders.

Build and own full-stack AI platform systems: backend services, data pipelines, frontends, and cloud infrastructure. Integrate LLM agents with lab data, instruments, and databases; prototype and harden agentic workflows; write well-tested code; mentor teammates and drive engineering best practices in a research-focused environment.

Develop and optimize LLM prompts, build scalable prompt-engineering frameworks, integrate AI solutions into applications, analyze user feedback, document best practices, and collaborate with domain experts and software developers. The role also contributes to LLM technology and platform decisions across GE Vernova business units.

Own operations for AI application infrastructure, including system monitoring, incident response, SLA/SLO governance, capacity management, help desk support, and production readiness. Lead root cause reviews, operational strategy, process improvements, and reliability initiatives across deployment and support workflows. Partner with development, operations, leadership, and external providers; oversee L1/L2/L3 support, mentor team members, and apply AI Ops, MLOps, DevSecOps, observability, and cloud practices.

Provide program support for AI and data strategy initiatives: coordinate meetings, prepare executive‑ready deliverables, manage and validate large datasets, support intake/tracking workflows (e.g., ServiceNow), maintain SharePoint knowledge repositories, monitor timelines and risks, and coordinate across teams to produce briefings for senior leadership.

Lead the engineering team for Orchestrator, an AI-powered healthcare referral coordination product. Own end-to-end delivery, architecture, EHR integration strategy, scalability, customer partnerships, hiring, and team development. Remain hands-on through design reviews, code reviews, and production incidents while building generalized solutions across healthcare customers. Required expertise includes EHR integrations, HL7 v2, FHIR, full-stack production engineering, and at least one year of engineering management experience.

Lead design and delivery of GenAI and BI data platform capabilities, driving data governance, metadata/lineage, pipeline engineering, tenant onboarding, platform adoption, and compliance across enterprise systems while mentoring engineers and partnering with stakeholders.