Top Artificial Intelligence Jobs

Develop and deploy applied AI systems: integrate models, build inference and evaluation pipelines, optimize performance for edge and adversarial conditions, create production-quality data and serving software, prototype with mission teams, and ensure reliability through testing, monitoring, and benchmarking.

Design, prototype, and ship production-grade AI solutions (LLMs, agents, RAG) integrated with enterprise systems. Define success metrics, build evaluation harnesses, and harden systems for scale, observability, and cost efficiency. Collaborate with product, design, and business stakeholders and contribute reusable tooling and components.

Design, implement, and scale finance and operational data infrastructure connecting Salesforce, Snowflake, and BI platforms. Automate ETL/ELT pipelines, build real-time executive dashboards showing SaaS financial metrics, establish source-of-truth systems, define data governance and metric standards, and enable self-service analytics for stakeholders across finance, product, and revenue operations.

Lead AI-driven search optimization to ensure Economist content is discoverable and cited by generative engines. Build strategies, run AIO audits, research prompts/keywords, analyze LLM search patterns, define measurement frameworks, and collaborate with editorial, product, engineering and analytics teams to scale AI optimization and drive organic subscriber growth.

Design, build, and integrate LLM/Generative AI capabilities into enterprise applications. Evaluate models and platforms, implement RAG/embeddings/vector search, build APIs, develop proofs-of-concept and production solutions, ensure secure/responsible AI, and collaborate with stakeholders to translate business needs into scalable AI systems.

Lead AI ROI engagements end-to-end: build and defend financial business cases, convert attribution data into CFO-ready ROI and payback metrics, pre-register success criteria, partner with analysts and engineering to capture needed data, and productize repeatable methodology.

Act as a subject-matter expert for RE NXT and AI capabilities to drive product adoption and customer success. Engage customers with demos, presentations, and enablement content; analyze usage and feedback; partner cross-functionally to shape messaging, adoption strategies, and product roadmap.

Support product development across internal CRM and fund/investor/advisor platforms. Maintain and prioritize backlog, write user stories and release notes, triage Jira Service Desk, perform QA, participate in sprints, manage multi-system requirements, gather stakeholder feedback, and support documentation and small-to-medium feature delivery.

Lead technical AI work to fine-tune, evaluate, and scale LLMs and ML pipelines for semantic metadata, recommendation and personalization systems. Build production-grade ASR, vector search, and BigQuery-based analytics; ensure model accuracy, ethical guardrails, bias mitigation, and secure cloud deployments while collaborating with cross-functional teams.

Design, integrate, and deploy AI-enabled automation and analytics to reduce cyber risk for Navy missions. Engage stakeholders to shape solutions, implement services/APIs and data integrations, document security-aligned designs, and follow RMF and secure engineering practices while working in classified or controlled environments.

Work with cybersecurity and mission teams to clarify problems, design and implement AI-enabled automation and integrations, develop scripts/services/APIs, integrate systems and data securely, produce documentation, and align solutions with RMF and authorization requirements in classified or controlled environments.

Lead the roadmap for AI Shopping conversion and social commerce journeys. Define product strategy, success metrics, and dashboards; analyze pixel and attribution data; run experiments; partner with engineering, design, analytics, SEO, and ops to reduce funnel friction, improve attribution, and grow revenue at enterprise scale.

Drive cross-functional technical programs to design, integrate, and deploy AI safeguards across model, platform, API, and cloud environments. Translate safety objectives into execution plans, own milestones and risks, coordinate engineering and policy partners, define metrics, and ensure readiness for sensitive deployments while balancing safety, usefulness, and operational constraints.

Design, execute, and operationalize fine-tuning workflows for large language models using supervised, preference-based, and reinforcement learning approaches. Build and operate distributed training pipelines, construct datasets, run rigorous evaluations, and collaborate with cross-functional teams to deliver production-grade LLM solutions while mentoring junior engineers.

Optimize throughput, latency, and cost for large-model training and inference across the stack, from kernel and compiler-level work to distributed system tuning. Profile and instrument GPU and distributed workloads, apply model-parallelism and memory-management strategies, and implement model-compression tradeoffs. Collaborate with cross-functional teams, perform code and design reviews, mentor junior engineers, and ship production-quality ML infrastructure improvements.

Design and ship production-ready machine learning systems by bridging applied research and engineering. Responsibilities include model training, fine-tuning, evaluation at scale, distributed and mixed-precision training, deployment with observability and safeguards, and adapting current research for high-impact production use.

Design, build, and maintain Azure-hosted web portals, CRM integrations, and the Argus EDI platform. Implement full-stack features (UI, API, data), integrate AI/automation, participate in Azure architectural decisions, and collaborate across TPA stakeholders to deliver scalable production solutions.

Monitor and analyze earned, owned, and social media performance; build dashboards and visualizations; support measurement frameworks and competitive analyses; audit brand visibility across generative AI platforms; analyze AI-generated responses and search trends; help develop AI-powered workflows and internal tools; conduct research on emerging AI technologies and collaborate with cross-functional teams to apply AI in communications.

Lead end-to-end technical direction and integration of an airborne product, coordinating systems, software, AI, computer vision, and verification teams to define architecture, resolve cross-disciplinary risks, and deliver FAA-ready avionics solutions on schedule and within program constraints.

Build and deploy AI-enabled workflow automations and agentic systems across the Office of the CEO, Capital Markets, Finance, and Operations. Integrate LLMs and foundation models with BI/data infrastructure, prototype and scale production-ready copilots and RAG solutions, and document/enable reusable templates while ensuring data security, governance, and stakeholder adoption.

Own TLDR's GTM systems end-to-end: HubSpot administration, attribution, pipeline data integrity, automations, system evaluations, and monitoring. Build dashboards and alerting, diagnose recurring issues, decide between quick-fixes or re-architecture, ship automations and agent workflows, and document operational runbooks. Partner with Applied AI to apply AI to GTM problems like attribution.

Support development and optimization of AI models for customer service automation through prompt engineering, fine-tuning, RAG, and agentic AI. Build deployment pipelines, evaluation/testing frameworks, and collaborate with engineering and business teams to integrate AI solutions into production while tracking model quality and metrics.

Bring up, benchmark, tune, and debug large-scale distributed LLM training and inference on multi-GPU/multi-node NVIDIA platforms. Build benchmarking, automation, triage, and failure-attribution tooling; perform root-cause analysis and deliver data-driven performance recommendations.

Build scalable Generative AI applications and microservices in an agile environment. Translate business requirements into high-quality code, participate in testing, CI/CD and deployments, and apply prompt engineering, RAG, model fine-tuning, and MLOps best practices. Collaborate globally on LLM-based solutions, containerized deployments, monitoring, and event-driven AI data pipelines while following Citis development and operational standards.

Review and evaluate AI-generated responses, annotate and validate text/conversations, identify linguistic and contextual issues, and ensure annotations meet quality and linguistic guidelines to improve LLM performance.