Top Artificial Intelligence Jobs

Drive technical sales for Microsoft’s cloud and AI data platform through demos, proofs of concept, hackathons, and architecture workshops. Advise enterprise customers on secure, scalable database and analytics architectures, migrations, and deployments. Build relationships with platform leaders, resolve technical blockers, collaborate with engineering, and promote Azure data technologies including Fabric, Databricks, Purview, and Azure databases.

Lead enterprise AI platform automation across infrastructure provisioning, CI/CD, governance, observability, deployment, and lifecycle management. Build self-service capabilities for GenAI, data science, data engineering, and analytics workloads using Terraform, Kubernetes, cloud technologies, Python, and DevSecOps practices. Support agentic AI, event-driven architectures, distributed computing, and operational reliability while collaborating with engineering, architecture, governance, security, and business stakeholders. Provide technical leadership, design reviews, mentoring, and best-practice guidance.

Develop AI and data solutions for aerospace engineering, including machine learning systems, proof-of-concepts, distributed data architectures, technical code, and production applications. Interns collaborate cross-functionally, present architectures and demos, apply cloud and database technologies, and use methods such as Game Theory and DecisionIQ to deliver business value. The role is remote and offers mentorship, project ownership, and potential return offers.

Lead commercialization and GTM for Mastercard's U.S. Mid-Market Accelerator partner-led solutions. Define regional strategy, identify and structure partners, secure approvals, drive cross-functional execution, manage contracts and Salesforce pipeline, monitor post-launch performance, and enable internal teams and issuers to accelerate adoption and revenue growth.

Build, ship, and operate backend services that deliver agentic and LLM-powered features in a multi-tenant platform. Implement agentic workflows, retrieval/RAG patterns, prompt management, and streaming responses. Own design, testing, CI/CD, deployment, observability, and production support while ensuring security, Responsible AI, and performance in a regulated environment.

Lead commercialization and go-to-market for Mastercard's U.S. Mid-Market Accelerator partner-led solutions. Define regional strategy, identify and structure partners, drive GTM and issuer engagement, manage cross-functional execution, track pipeline and performance, and enable internal and partner teams to scale adoption and revenue for B2B payment use cases.

Lead Copia's AI product line: own Copilot, define AI roadmap from code intelligence to autonomous agents, write specs, partner with engineering, GTM, and CEO, and monetize AI features for industrial automation customers.

Design, build, and productionize AI systems (genAI, agents, RAG) with observability, model quality, governance, and responsible-AI controls. Implement serving/inference, cloud deployments, model registry/experiment tracking and cross-functional leadership to scale claim-edit automation and agentic workflows.

Individual contributor lead role to architect, build, and deploy agentic LLM systems, RAG/hybrid search, and scalable microservices on cloud/Big Data platforms. Owns MLOps/LLMOps, evaluation frameworks, HIPAA-compliant security controls, and integrations with data and internal AI platforms. Partners with data engineering, mentors engineers, prototypes POCs, and drives production reliability and cost KPIs.

Lead and build an Enterprise AI organization to define strategy, platforms, governance, and standards. Deliver scalable AI platforms (LLM gateways, model registries, vector DBs), enable business adoption, embed responsible AI and compliance, partner across Security, Legal, Finance, and drive measurable AI use cases and ROI while managing a small, cross-functional AI architecture and engineering team.

Design and build AI agents, first-party web and mobile apps, and scalable backend services. Integrate LLM frameworks and enterprise systems, implement APIs and microservices, ensure observability and security, and collaborate with Finance, Security, and Engineering stakeholders.

Lead development of next-generation entity resolution using transformers, graph ML, and deep learning at scale. Partner with engineers and product to design, train, validate, and deploy models on AWS/Spark, leverage agentic AI workflows, and translate technical work into business impact across enterprise use cases.

Lead development of next-generation entity resolution solutions using transformers, graph ML, deep learning, and big-data tooling. Partner with engineers and product to design, train, evaluate, validate, and deploy ML models at scale; leverage Agentic AI workflows; translate technical work into business impact across enterprise use cases.

Design and architect the core distributed platform powering production AI agents: orchestration, execution, memory, tool-calling, workflows, reliability, observability, and developer experience. Drive architecture decisions, set engineering standards, mentor engineers, and lead cross-functional initiatives to translate AI capabilities into production-ready platform features.

Lead and grow a technical team responsible for verifying AI compilers for next-generation workloads. Define formal verification requirements, drive AI-assisted and compiler-aware verification (fuzzing, differential testing, symbolic reasoning, formal methods), and partner with compiler, CUDA, runtime, and ML framework teams to build scalable verification infrastructure and improve production confidence.

Own end-to-end development of AI product demos, including React and TypeScript interfaces, streaming chat and agent workspaces, backend-for-frontend services, database schemas, deployment, observability, and on-call. Partner on app-builder tooling, design systems, agent-facing CLI and deployment experiences, accessibility, localization, audit trails, and enterprise readiness. Debug across backend and Kubernetes infrastructure while delivering reliable, AI-native product experiences.

Fine-tune and align LLMs for specialized financial tasks; build agentic solutions with LangChain/Autogen; engineer ML pipelines for large financial datasets; deploy, monitor and scale production models; track AI research and integrate emerging tooling.

Build and test Marvis Minis across embedded firmware and cloud services. Develop Minis features, author network/application validation tests, debug cross-layer issues spanning AP firmware to cloud data pipelines, extend platform to additional devices, and maintain cloud processing components (Storm, Airflow, Kafka, Redis, Elasticsearch). Collaborate with firmware, data science, QA, and operations and participate in production rollouts and incident response.

Supports Qnity’s enterprise-wide adoption of Microsoft Copilot and related AI tools. Responsibilities include coordinating adoption campaigns, creating awareness and training materials, supporting demos and orientation sessions, maintaining learning resources, tracking participation, collecting feedback, onboarding AI champions and trainers, managing community activities, and preparing usage reports to improve adoption initiatives.

Own the AI inference and runtime platform, including open-weight model serving, Kubernetes operators and autoscaling, GPU infrastructure, stateful vector and graph stores, and secure microVM-based agent sandboxing. Build Rust, Python, and Kubernetes control-plane services; manage infrastructure as code, observability, metering, lifecycle operations, upgrades, backups, and failover. Establish security controls, open-source standards, and reliable production operations across cloud and managed GPU environments.

Design and build production-grade GenAI solutions: RAG pipelines, agentic multi-agent workflows, knowledge-base chatbots, vector DBs, LLM integrations, observability, PII redaction, and deploy AI backends with monitoring and documentation.

Design, build, and deploy scalable ML pipelines for multimodal neuroscience data. Research, train, and deploy advanced deep learning and reinforcement learning models to predict brain states, detect pathological patterns, and generate personalized neuromodulation parameters. Select AI frameworks and cloud infrastructure, collaborate with computational neuroscientists, and contribute to IP and publications.

Design, implement, and deploy end-to-end machine learning and AI solutions. Build data pipelines, train and evaluate models, write production-quality code, and operationalize capabilities in cloud or Linux environments while working with clients and following software engineering best practices.

As a Data Scientist, you'll analyze and interpret complex data sets, develop algorithms, and work with clients to provide actionable insights, utilizing tools from machine learning and AI.

Design, implement, test, and maintain full‑stack software across backend and frontend. Participate in full product lifecycle, review peer code, create documentation, collaborate globally, and help drive strategic goals.