Top Artificial Intelligence Jobs

Design and build agentic LLM applications and multi-agent workflows: tool-calling, orchestration, prompt and context management, RAG, evaluation pipelines, observability, SLOs, and production-grade, cloud-native backend services. Collaborate with product, UX, applied science, and infra to ship reliable, secure AI features end-to-end.

Lead strategy and execution for Binti's AI and core platform capabilities. Own roadmap, scale LLM-based tools (transcription, form pre-fill, case chat), partner with engineering and design, prototype rapidly with AI-native tools, conduct user research with social workers, and drive platform health and AI adoption metrics.

Build and operate backend infrastructure and agentic AI platforms on Azure. Own data, identity, CI/CD, observability, multi-model LLM orchestration, cost/token economics, MCP tooling, and SLO-driven production reliability for autonomous multi-agent systems.

Lead architecture and platform engineering for a real-time, highly available API platform; design event-driven, observability, service-mesh, and AI-integrated systems; drive agent-based automation, LLM orchestration, developer enablement, and production-scale AI deployment while defining governance and evaluation metrics.

Develop and deploy machine learning solutions for autonomous truck applications: EDA, deep learning model development, embedded deployment, data ingestion/curation, analytics, visualization, technical leadership, and process improvement across manufacturing and business domains.

Lead intake, prioritization, and value management of enterprise AI initiatives. Translate strategy into a prioritized, delivery-ready AI portfolio, shape problem statements, align stakeholders, integrate governance and risk, track realized value, and advance portfolio management maturity.

Design, build, and operate AWS-native AI/ML systems: RAG pipelines, fine-tuned LLMs, and microservices. Implement IaC (Terraform), CI/CD, policy guardrails, and production deployments integrating Bedrock/SageMaker, LangChain, and telemetry for secure, scalable enterprise use.

Lead client-facing AI program governance and delivery as PMO manager. Translate business objectives into AI roadmaps and OKRs, oversee AI/ML lifecycle, ensure data readiness, model validation, deployment, and responsible AI compliance. Manage PMO standards, portfolio prioritization, AI-augmented reporting, and lead executive communications while mentoring PMs, TPMs, and analysts.

Support compensation program administration, analysis, and modernization through HR systems, automation, analytics, and AI-enabled tools. Partner with HR, Finance, and Technology to manage market pricing, job architecture, compensation cycles, data integrity, reporting, process improvements, governance, and stakeholder communications to ensure equitable, compliant, and scalable compensation programs.

Lead and grow FAS's AI policy portfolio by setting strategy, managing staff and budgets, overseeing grant-funded projects, building cross-sector partnerships, fundraising, and advising policymakers on AI safety, fairness, responsible adoption, and AI for public missions.

Postdoctoral researcher will apply and develop AI/ML methods to integrate and analyze large-scale multiomic datasets (bulk, single-cell, spatial, multimodal), train foundation and self-supervised models, build interpretable predictive models of host-response and disease trajectories, collaborate in multidisciplinary teams, publish results, and present findings.

Design, build, and deploy AI-driven Salesforce applications and autonomous agents using Apex, LWC, Flows, Agentforce, Einstein AI, Prompt Builder, and Data Cloud. Implement generative AI, LLMs, RAG, integrations, automation, and analytics; ensure security, governance, and responsible AI; collaborate with product, UX, and DevSecOps teams in Agile delivery.

Build and ship internal AI frameworks and staff-facing tools for care navigators: design the Navigator Workbench, optimize data feeds and transcripts, define prompts/decision trees and safety guardrails, integrate with engineering and data architecture, monitor AI output quality, and enable non-technical ops to extend systems.

Build production-grade, generative AI features enabling natural-language interactions and AI agents. Work on scaling ingestion, real-time querying/notification, and fast search across terabytes of data. Collaborate with deployment teams and users to iterate on safe, impactful AI-driven user experiences.

Design, build, and support an AI-ready enterprise data enablement platform using Databricks, dbt, Delta Lake, and Azure data services. Create reusable CI/CD and IaC accelerators, govern data products via Unity Catalog, enable analytical access with Starburst/Trino, troubleshoot production issues, and collaborate with cross-functional teams to deliver governed, reusable, and production-grade data products for analytics and GenAI use cases.

Lead design, training, and deployment of small, efficient language models and agentic systems for security: threat detection, policy enforcement, real-time inference, edge deployment, adversarial robustness, and productionization in security pipelines.

Design and build the data and analytics layer: schemas, pipelines, APIs, dashboards, forecasting and ML models. Deliver usage-based metering, ensure data correctness and observability, and ship customer-facing analytics while raising engineering standards.

Lead RTL development and microarchitecture of an AI-enabled RISC-V CPU core, including vector and matrix engines. Drive design for performance, power, area, verification support, and SoC integration while collaborating with cross-functional teams to meet timing, reliability, testability, and power goals.

Lead cross-functional initiatives to increase engineering productivity via AI-assisted development tools, workflows, and dashboards. Drive adoption, define productivity metrics, enforce governance and cost controls, coordinate automation (test generation, incident workflows), run enablement programs, and report executive-level impact and roadmap progress.

Lead research on agentic multimodal and video reasoning systems, advance computer vision and generative-AI techniques, design scalable privacy-preserving models, evaluate performance in real-world settings, build proof-of-concepts with engineers and product, publish and patent findings, and mentor junior scientists.

Lead and execute organizational change management activities for ServiceNow and AI-enabled transformations: stakeholder and impact assessments, communications, training, governance, metrics, content/repository management, and adoption reporting across multiple programs.

Provide legal and compliance support for AI across its lifecycle: risk assessments, governance framework implementation, contract review, regulatory tracking, training, and audit readiness to enable responsible AI adoption in a regulated life sciences environment.

As an AI/ML Engineer, you'll develop data/ML pipelines, analyze unstructured data, and contribute broadly to product development.

Design, train, evaluate, and optimize agent-native LLMs and RAG pipelines for enterprise use. Build training and RL pipelines (RLHF/DPO/PPO), embedding-based memory, evaluation harnesses, observability, and inference optimization across cloud and on-prem environments.

Build and operate secure, scalable ML pipelines and hybrid GPU infrastructure for training, fine-tuning, serving, and governing open-source LLMs and agentic apps. Implement CI/CD, model versioning, observability, benchmarking, security controls, and integration with RAG/agent tools and vector DBs to support production deployments across tenants.