Top Artificial Intelligence Jobs

Design, build, and scale AI-enabled cloud infrastructure including RAG pipelines, vector DBs, and multi-agent flows. Implement IaC (Terraform/ARM), Kubernetes (AKS/EKS), containerization, automation (Python/PowerShell), cloud security controls, and collaborate with cross-functional teams to deploy production LLM workloads.

Lead cross-functional programs to deploy AI servers from prototype to volume production. Manage roadmaps, suppliers, ODMs, procurement, manufacturing, risk, budgets, and stakeholder communication to ensure on-time, production-ready delivery of compute infrastructure.

Lead product strategy and P&L for hyperscale AI connectivity products (Dark Fiber, Wave, eLine). Translate AI workload and GPU networking needs into scalable connectivity solutions, drive GTM and pricing, enable sales for large enterprise and hyperscaler deals, and align cross-functional teams on KPI-driven roadmaps.

Collects semen from males and inseminates hens, inspects semen quality/quantity using microscope and densimeter, monitors hen production, prepares and maintains equipment, replaces males based on fertility, and performs biosecurity, sanitation, and other assigned production tasks (including humane euthanasia when required). Role requires intensive physical activity and adherence to animal welfare and biosecurity protocols.

Design, build, and operate large-scale AI training and inference platform layers including GPU clusters, distributed training frameworks, schedulers, storage, networking, and developer tooling with emphasis on reliability, efficiency, and cost control. Collaborate with ML engineers to productionize workloads and apply strong software engineering practices.

Design, optimize, and deploy machine learning models for resource-constrained edge devices. Focus on model compression, quantization, hardware-aware optimization, profiling, and production deployment to mobile, embedded, and accelerator platforms while ensuring on-device privacy, security, and cross-functional collaboration.

Lead strategy and execution for Hearst's enterprise AI content platform, building semantic enrichment, embeddings, knowledge graphs, metadata, and retrieval pipelines to power search, personalization, recommendations, and generative AI. Define product vision, prioritize platform capabilities, evaluate models and vector databases, and translate AI concepts into roadmaps while aligning cross-functional stakeholders.

Lead internal adoption and enablement for the AI Center of Excellence: translate technical capabilities into plain-language value, design launches and training, build champion networks, run feedback loops, track adoption metrics, and partner with team leads to embed tools into workflows.

Lead end-to-end AI product lifecycle for financial services: identify automation opportunities, define roadmap and KPIs, prioritize backlog, partner with engineering and data science to deploy models, ensure governance/compliance and production readiness, drive adoption and measure business impact, communicate with executives, and mentor product owners.

Convert demand forecasts into per-tranche requirements, qualify deliverability pre-signature, run and track parallel bring-ups across clouds and sites, own the contract-to-occupied lifecycle, automate readiness, and publish executive reporting on timing, utilization, and paid-idle metrics.

Lead enterprise AI architecture strategy and roadmap; design scalable AI, data, and integration architectures; establish reusable frameworks, standards, and governance; drive platform and technology selection; ensure security, compliance, and responsible AI; partner with business leaders and mentor teams to deliver enterprise-scale AI solutions.

Lead and execute complex technology, data, and AI-focused audits across AI/ML, generative AI/LLMs, cloud platforms, and analytics. Design risk-based audit programs, assess AI governance and model risk, apply data analytics and automation, report findings to stakeholders, and mentor audit team members while advancing AI-enabled audit capabilities.

Design, prototype, and deliver AI-enabled products, applications, APIs, and intelligent workflows. Build scalable Azure-based solutions using LLMs, agents, data pipelines, and CI/CD while ensuring security, governance, monitoring, and responsible AI practices. Collaborate with business and engineering stakeholders to define requirements, experimentation, and evaluation frameworks to drive measurable business value.

Design, develop, and operationalize full-stack, cloud-native applications for transaction monitoring and data controls. Build responsive UIs and Java-based microservices, integrate event-driven pipelines (Kafka, Spark), deploy on GCP (App Engine, GKE, Cloud Run), and implement LLM/Agentic AI and RAG automation while ensuring performance, resiliency, observability, and security.

Build and evolve a cloud-native developer platform focusing on self-service "Golden Paths", GitOps/CI-CD tooling, observability, and AI enablement. Partner with Security and Infrastructure, mentor engineers, drive platform strategy and adoption, define reliability (SLO/SLI) and incident response, and treat the platform as a product with strong developer empathy.

Serve as embedded technical lead to design, build, and demo production-grade agentic AI solutions for slot/casino game production. Deliver prototypes on real game stacks (math engine, backend, client), create transferable agentic harnesses/loops, own model selection, deployment, cost/monitoring, and lead knowledge transfer and client-facing technical engagement.

Design and deliver enterprise-grade AI/GenAI solutions on Salesforce or ServiceNow. Lead RAG pipelines, LLM workflows, NLP automation, and vector retrieval integrations. Architect scalable API/microservice integrations, ensure performance, security, and reliability, and define AI governance and guardrails. Collaborate with product, engineering, and customers through discovery, workshops, and demos to operationalize AI for Field and Customer Service workflows.

Design, build, and deploy AI-powered applications that support teachers and families. Combine ML models, high-quality data, and human-centered workflows into production-ready tools. Own end-to-end lifecycle from research and prototyping to deployment, monitoring, evaluation, and safety/guardrails to ensure ethical, reliable AI features.

Lead design and operation of the enterprise data platform, pipelines, semantics, and AI systems. Build scalable BigQuery-based infrastructure, govern metrics and semantic layer, deliver self-service analytics, enable agentic AI workflows, manage costs, and hire and lead a cross-functional data and AI engineering team to produce trusted, actionable insights across channels.

Design, build, and deploy LLM-powered applications, agents, and automation pipelines. Implement RESTful APIs, write production-quality code (Python, React, Node.js), deploy on AWS/Heroku with CI/CD, and partner cross-functionally to drive acquisition, retention, and operational efficiency.

As a Founding AI Backend Engineer at Pakket, you'll enhance backend systems, debug the CV pipeline, and deploy services across warehouses, ensuring operational reliability and rapid feature deployment.

Conduct applied AI research to develop trustworthy, operationally relevant AI capabilities and prototypes across defence and safety-critical domains. Design and evaluate advanced ML methods, build reproducible research pipelines, embed Responsible AI practices, produce technical reports/publications, and transition research outputs to engineering teams while supporting bids, demos and stakeholder engagement.

Design, develop, and deploy production-ready AI systems (deep learning, NLP, computer vision). Build APIs and microservices, integrate models into software stacks, optimize inference performance, maintain and retrain models, and research generative AI and advanced ML techniques.

Own and improve analytics datasets and ETL pipelines powering an internal product analytics AI agent. Build retrieval, knowledge, evaluation, and backend agent workflows. Improve retrieval and answer quality, develop evaluation systems, and create user-facing features. Partner with data scientists to turn analytics needs into scalable capabilities and help set roadmap and technical direction for the data and quality side of the platform.

Design and build a production multi-agent LLM framework and LLM-powered pipelines (text-to-SQL, NLU, structured reasoning). Implement RAG, prompt systems, evaluation and CI/CD for AI features, instrument and optimize model performance, and collaborate with product to deliver user-facing capabilities.