Top Artificial Intelligence Jobs

Design and operate scalable, event-driven backend systems in Python to power agentic AI workflows and real-time financial infrastructure. Own APIs, data architecture, reliability primitives, and platform foundations while partnering with ML and product teams to productionize AI-native financial systems.

Design and operate scalable, event-driven backend systems in Python for AI-native financial products. Build typed APIs (FastAPI + Pydantic), own data and ledger architectures, develop reliability primitives, partner with ML teams to productionize models, and ship platform foundations for audited, high-throughput financial workflows.

Design, build, and deploy production AI agents and orchestration systems using tools like LangChain and LlamaIndex. Apply prompting, RAG, fine-tuning, and model selection. Architect agent reasoning and workflows, implement monitoring and evaluation, and integrate AI systems with backend and frontend services for scalable, reliable, compliant production deployments.

Build and lead internal AI-driven security tooling and automation, support incident response with threat-hunting tools, maintain Kubernetes/serverless and ETL/log pipelines, partner with product teams to reduce risk, and mentor engineers.

Design and deploy agentic AI systems that automate multi-step financial workflows. Architect agents using frameworks like LangChain and LlamaIndex, apply prompting, RAG, and fine-tuning, optimize reasoning and tool use, build feedback-driven systems, and integrate AI reasoning securely into production backend and frontend services.

As a Knowledge Development Engineer, you'll architect and deploy AI-driven production systems, reverse-engineer existing systems, and guide product development. You'll synthesize customer feedback and lead experiments to connect technical development with business outcomes.

The Knowledge Development Engineer leverages AI agents to solve customer problems by building applications and automating workflows, focusing on delivery speed and quality.

Lead product strategy and corporate development for AI initiatives at Datadog. Define AI product direction, evaluate market opportunities, partnerships, and M&A, collaborate with cross-functional teams, and support go-to-market and roadmap decisions to accelerate AI capabilities across the platform.

The Senior AI Engineer will build and scale AI features, develop AI agents, and partner with product teams to enhance products for small businesses.

As an AI Engineer, you will create a unified evaluation infrastructure for AI agents, ensuring reliable performance while integrating automated pipelines and feedback systems for rapid model evaluation and quality assurance.

Lead and conduct ML research aligned to Sprinter Health's strategy: define research agenda, design rigorous experiments, develop novel methods and evaluations, publish and patent findings, translate research into production-ready tools, and collaborate with clinicians and cross-functional teams to validate and deploy clinically robust AI solutions.

Design and build scalable, automated evaluation pipelines and infrastructure to validate speech, audio, and multimodal models. Translate research benchmarks into enforceable pass/fail gates, run batch and streaming tests, operate canaries and continuous monitoring, integrate quality gates into CI/CD, and partner with research, training, inference, and infra teams to prevent regressions and ensure production model quality.

Build and deploy production-scale AI/LLM systems: own ML lifecycle from model development to serving, optimize GPU/Kubernetes deployments, design LLM applications and APIs with monitoring, apply CI/CD and version tracking, collaborate with product teams, and contribute to AI engineering best practices.

Lead the AI research agenda at Kepler, focusing on trustworthy AI for enterprise decisions. Oversee research on agentic systems and evaluation frameworks, and manage a research team. Ensure production deployment of innovative AI solutions based on real financial data.

Lead the design and implementation of agentic AI and LLM applications within Mastercard's Operational Intelligence team, focusing on backend and production-scale solutions. Mentor engineers and ensure high reliability and observability of systems. Develop integration between AI platforms and enterprise data services.

As a Senior Data Scientist at Capital One, you will lead AI-powered product development using machine learning technologies, focusing on NLP and large language models to enhance customer experiences.

Lead and grow a team building Klaviyo's Customer Agent conversational AI platform. Set technical direction, own roadmap, collaborate cross-functionally, guide system and model design, ensure reliability, hire and develop engineers and managers, and deliver scalable, AI-driven customer engagement features.

As a Senior AI Engineer at Klaviyo, you will design and build scalable backend systems and AI solutions, collaborating with product managers and engineers to enhance AI technologies for optimizing customer experiences.

Owns AI-assisted engineering across accelerator software teams, embedding agentic workflows into coding, debugging, triage, testing, documentation, CI, and silicon-debug processes. Builds shared agent tooling, maintains evaluations, measures productivity gains, and establishes standards for reliable AI-native development. Requires expert experience with Codex, Claude Code, LLM APIs, evaluations, and adopted developer tooling, plus strong systems or infrastructure credibility.

Lead large-scale SRE initiatives, define reliability and platform roadmaps, and build resilient distributed systems for AI-driven products. Design AI agents and skills for platform operations, improve automation and observability, and collaborate across cloud, security, platform, and AI/ML teams. Troubleshoot complex systems, drive incident and postmortem practices, and mentor engineers. The role requires deep expertise in systems architecture, networking, Kubernetes, public cloud, infrastructure as code, and automation.

Own cross-team, platform-level engineering initiatives end-to-end: architecture, migrations, observability, on-call, and customer outcomes. Drive AI-native tooling and agentic AI adoption, mentor senior engineers, and set technical standards across teams.

Architect and deliver high-scale NVIDIA GPU compute environments for enterprise AI workloads. Lead bare-metal provisioning, cluster management, operating-system hardening, scheduler and workload configuration, monitoring, performance validation, and day-two operations. Serve as a customer-facing technical authority, supporting presales discovery, architecture, bills of materials, estimation, and client workshops. Design repeatable AI infrastructure across DGX, HGX, MGX, NVL72, and Cisco AI POD platforms.

Architects and delivers high-performance AI networking fabrics using NVIDIA InfiniBand and Spectrum-X Ethernet. Designs lossless, automated, scalable network infrastructure for DGX and AI cloud environments; configures routing, QoS, RoCEv2, SHARP, telemetry, and DPU offload. Owns cabling, link validation, performance testing, infrastructure-as-code, and day-two operations. Provides technical leadership in client workshops, presales scoping, bills of materials, cost estimation, and integration with enterprise networks.

Leads architecture, provisioning, automation, performance validation, and day-two operations for large-scale NVIDIA GPU clusters and AI compute environments. Designs repeatable bare-metal and Kubernetes-based infrastructure using schedulers, monitoring, networking, and infrastructure-as-code. Serves as the technical authority for DGX, HGX, MGX, NVL72, and Cisco AI POD deployments, while supporting pre-sales discovery, bills of materials, solution estimates, client workshops, and executive-level communication.

Architects and deploys high-performance NVIDIA InfiniBand and Spectrum-X Ethernet fabrics for AI infrastructure. Responsibilities include fabric topology, routing, congestion control, RoCEv2, DPU offload, telemetry, automation, cabling, optical validation, performance testing, and infrastructure-as-code. Leads client delivery, pre-sales scoping, bills of materials, technical workshops, and integration with enterprise networks. The role requires translating complex network architectures into scalable deployments and commercial outcomes.