Top Artificial Intelligence Jobs

Lead the design and delivery of enterprise AI platform capabilities and production agentic AI solutions. Build multi-agent systems, RAG and knowledge management solutions, cloud-native infrastructure, and MLOps/LLMOps capabilities. Partner with business, architecture, security, legal, and risk stakeholders; influence architecture decisions; mentor engineers; and establish reusable engineering, governance, monitoring, and operational practices.

Build enterprise AI platform capabilities and production AI applications, including agentic workflows, retrieval-augmented generation, semantic search, vector databases, cloud-native services, and AI model operations. Collaborate with senior engineers on orchestration, developer tooling, governance, security, responsible AI, monitoring, testing, and compliance. Use Python, Google Cloud Platform, Kubernetes, CI/CD, and infrastructure as code while developing scalable AI solutions.

Designs and delivers enterprise AI platform capabilities and production-ready agentic applications. Builds multi-agent systems, RAG and knowledge platforms, orchestration frameworks, cloud-native services, and developer tooling. Implements MLOps and LLMOps for deployment, evaluation, monitoring, observability, and optimization. Partners with stakeholders, contributes to architecture and engineering standards, and independently owns initiatives from design through production deployment.

Lead design, build, and productionization of scalable, secure data pipelines and products in Azure Databricks. Implement batch and streaming ETL, Vector Search, RAG, LLM integrations, automation with Cursor/agents, CI/CD, observability, and governance. Mentor peers and partner with data scientists to operationalize AI while owning platform reliability and efficiency.

Build production AI agents and agent-powered features from research and prototyping through deployment. Develop evaluation infrastructure with golden datasets, regression suites, and offline/online scoring. Own retrieval and context pipelines, production instrumentation, failure taxonomies, and cost and latency budgets. Partner with data science and product management on experiments and agent behavior, while establishing LLM engineering patterns, guardrails, and reusable tooling.

Develop and maintain high-performance Linux sensor components in C/C++ to secure containerized cloud workloads. Design and implement eBPF-based features, ensure reliability and performance, debug and test at scale, collaborate across teams, lead container and orchestrator security initiatives, and participate in code reviews and CI/CD practices.

Leads an engineering team developing AI-native cybersecurity products and capabilities. Defines technical vision and architecture for agentic AI, LLM, retrieval, reasoning, and autonomous security solutions. Drives concepts from rapid experimentation through enterprise production while ensuring security, reliability, scalability, governance, and evaluation. Partners with product, UX, researchers, customers, and executives; remains hands-on in architecture and implementation; and builds a high-performing team across AI/ML, distributed systems, and cybersecurity.

Design, build, test, and maintain scalable, secure data pipelines and architectures. Update data models, advise on/configure tools, validate controls, use SQL/NoSQL, and apply enterprise AI capabilities to accelerate and validate data engineering workflows while ensuring traceability and data sensitivity handling.

Lead the design, development, deployment, and support of foundational AI systems, including LLM training and inference, similarity search, guardrails, evaluation, governance, and observability. Develop optimization techniques that improve scalability, cost, latency, throughput, and hardware utilization. Partner with engineering, research, product, and program teams while contributing to AI platform strategy, technical vision, and long-term roadmaps.

Lead the design, development, testing, deployment, and support of foundational AI systems, including model training, LLM inference, similarity search, guardrails, evaluation, governance, and observability. Optimize large-scale production AI systems for scalability, latency, throughput, cost, and hardware utilization. Collaborate with engineering, research, program management, and product teams while shaping the technical vision and roadmap for responsible AI platforms.

Develop novel AI/ML solutions for healthcare using structured and unstructured data. Build and test agentic, generative AI, deep learning, and traditional ML systems in distributed environments. Design experiments, perform statistical analysis, visualize data, communicate findings, and collaborate with stakeholders, responsible AI teams, and engineering groups on compliant solutions, data pipelines, MLOps, LLMOps, and Agentic Ops. Document models and contribute to patents and publications.

Lead architecture and technical strategy for an agentic AI claims automation platform. Define end-to-end architecture, enable multi-agent workflows, integrate models and MLOps, ensure governance, resilience, observability, and cost efficiency, and mentor architects while aligning with enterprise AI standards.

Build and support full-stack AI solutions using Python, AWS AI services, APIs, integrations, and agentic workflows. Responsibilities include testing, debugging, technical documentation, deployment support, production readiness, and applying information security and data privacy practices, including GDPR and CCPA compliance.

Lead development of AI platform capabilities: build scalable backend services, engineer secure data/search pipelines (RAG, embeddings, OpenSearch), integrate AI-assisted engineering practices, deploy cloud-native solutions on AWS with Terraform, and mentor teams to improve code quality, reliability, and observability.

Design and deploy machine learning systems for data analysis, document management, client intelligence, NLP, speech recognition, forecasting, reinforcement learning, and recommendations. Build scalable ML frameworks, conduct experiments, evaluate and monitor models, perform A/B testing, and deploy production solutions across cloud platforms. Develop CI/CD pipelines, containerized services, automated hyperparameter searches, testing frameworks, and data quality metrics while collaborating with partner teams.

Lead an agile team to design, build, and deliver secure, scalable software. Develop production-quality code, architectures, data models, ETL pipelines, and diagnostics. Analyze large datasets to improve code and systems, troubleshoot complex issues, and contribute to engineering communities and best practices.

Design and implement foundational data systems for AI, focusing on efficiency and performance at scale. Collaborate on systems that optimize data handling and contribute to research advancements.

Leads global People Operations transformation across service delivery, Workday and workforce technology, AI automation, process improvement, data governance, and complex cross-functional programs. The role redesigns employee lifecycle workflows, builds scalable operating models, establishes governance and service standards, and translates workforce data into executive decisions. It requires driving transformation, M&A and restructures, technology implementations, and measurable improvements in operational performance and employee experience.

Build, test, document, launch, and support AI-powered tools, prompts, skills, agents, automations, and workflows for DC Operations. Partner with field technicians to gather requirements, maintain intake/tracking, evaluate opportunities, facilitate training, provide day-to-day support, track tool usage and impact, and convert field-built solutions into documented, supported assets.

Embed with customer AI teams to design, prototype, and productionize AI agents using W&B Weave. Advise on architecture, build reference implementations and demos, run technical workshops, gather field feedback for product roadmap, and troubleshoot customer environments.

Analyze retailer sales, shipment, consumer, pricing, promotion, and competitive data to identify growth opportunities for Best Buy and Canadian Tire. Develop sell-in stories, present insights to retail buyers, support forecasting, and influence sales, marketing, product, and supply chain decisions. Use Excel, Power BI, syndicated data, generative AI, and automation tools to streamline reporting and deliver actionable commercial recommendations.

Design, develop, and deploy AI-native software products that transform large-scale geospatial data into actionable intelligence. Build multi-agent workflows using orchestration frameworks and large language models, engineer tool-using and self-refining agents, develop data ingestion pipelines, and operate secure, scalable AI systems in production. Apply DevOps, reinforcement feedback mechanisms, and AI-assisted development practices while contributing to engineering standards and documentation.

Own an AI portfolio across a business domain, identify and prioritize opportunities, and personally build production agentic solutions end to end. Establish reusable patterns, evaluations, human-in-the-loop controls, and measurable business outcomes. Partner with functional leaders, earn stakeholder trust, guide platform choices, and improve team capability through mentoring, design and code reviews, and interviewing. Build reliable, secure, scalable applications around AI systems across frontend and backend layers.

Build and deploy end-to-end AI solutions embedded within business teams. Identify high-value problems, rapidly prototype agentic applications, integrate AI with business workflows, and measure business impact. Partner with principal engineers and platform teams on architecture, drive stakeholder adoption, communicate across technical and business audiences, and mentor earlier-career builders. The role requires hands-on full-stack prototyping, API integration, production deployment, and ownership from discovery through adoption.

Lead and architect large-scale AI and automation programs, set Agile delivery models, manage end-to-end delivery from concept to production, oversee AI-specific risks and governance, influence senior leadership on AI strategy, and drive innovation and adoption across the organization.