Top Artificial Intelligence Jobs

Staff Engineer responsible for architecting and implementing secure, scalable full-stack cloud services processing petabytes of data. The role provides technical direction across frontend, backend, and data layers; resolves complex cross-organizational challenges; establishes engineering standards for performance, reliability, and security; aligns technical execution with product strategy; and mentors senior and lead engineers. The position requires extensive experience with cloud-native distributed systems, architecture, technical leadership, cross-team influence, and large-scale reliability.

Lead the architecture and development of MHC’s data platform, including ingestion, modeling, governance, pipelines, and feature stores. Build production AI and LLM capabilities such as document understanding, RAG, embeddings, evaluation, and intelligent product features. Manage and mentor a small data and AI engineering team, establish technical standards, define roadmaps, and partner with Product, Engineering, and business stakeholders.

Lead AI product strategy and deliver AI-powered features for a healthcare workforce SaaS. Translate AI concepts into user experiences, run discovery with customers, collaborate with data science and engineering, use LLM tools in daily workflows, define success metrics and experiments, ensure ethical AI practices, and evangelize AI capabilities internally and to customers.

Design and build agent-based AI systems, including single- and multi-agent architectures, tool-use workflows, feedback loops, planning, memory, delegation, evaluation, and autonomous processes. Improve reliability, observability, and failure recovery while exploring long-running and self-improving systems. Candidates should independently build and test projects, evaluate agent performance, follow current AI research, and understand how AI components interact.

Build scalable, responsive frontend experiences for Snowflake Cortex AI. Develop production-quality TypeScript and React interfaces, integrate APIs supporting AI inference and data retrieval, participate in architecture and code reviews, collaborate with product and design teams, and optimize frontend performance and reliability. The role also involves using AI coding assistants as part of daily development and may include building LLM-powered applications or conversational interfaces.

Supports enterprise data governance across business domains by maintaining metadata, lineage, governance documentation, data quality records, dashboards, controls, and risk remediation activities. Partners with business and technology stakeholders to gather requirements, document governance needs, support domain and system onboarding, monitor governance metrics, and assist with RCSA and compliance initiatives. The role focuses on data transparency, quality, regulatory compliance, and emerging AI dataset governance and provenance practices.

Supports enterprise data governance and data risk management across business domains. Responsibilities include implementing governance policies, onboarding data assets, managing metadata and lineage, supporting governance tools, tracking KPIs, facilitating stakeholder sessions, translating business requirements, and coordinating risk and control assessments. The role also identifies data quality issues, monitors controls, documents remediation activities, and communicates governance progress, risks, and dependencies.

Architect and govern Azure-based event-driven integration platforms and an AI-powered Snowflake data intelligence platform. Design APIs, routing, transformation, observability, security, semantic layers, RAG memory, inference, recommendation, and agent orchestration systems. Lead architecture workshops, roadmaps, estimates, documentation, stakeholder presentations, and engineering mentorship while ensuring compliance with financial services governance and security requirements.

Design, configure, and maintain enterprise AI governance platforms and workflows (evidence capture, controls, reporting). Build integrations and automations with enterprise systems, enable metrics and data quality, translate Responsible AI policies into operational controls, coordinate vendors, and deliver training and enablement to drive adoption and audit readiness.

Design and implement technical AI governance frameworks for generative and agentic AI systems. Assess ethical, safety, security, and sociotechnical risks; evaluate architectures, data flows, permissions, tool integrations, and human oversight; and develop practical mitigations and controls. Partner with engineering, product, legal, security, and business teams to embed responsible AI practices, conduct red teaming and risk assessments, support regulatory alignment, and advance AI literacy across the organization.

Leads end-to-end AI and data science consulting engagements, from problem framing and prototyping through production implementation. Combines hands-on development, solution architecture, code review, and technical troubleshooting with client advisory, team leadership, mentoring, business development, and executive communication. Translates ambiguous business challenges into scalable AI, machine learning, optimization, and data strategies while managing multidisciplinary delivery teams and contributing to thought leadership.

Leads the design, implementation, and production deployment of AI-powered solutions across technology teams. Responsibilities include architecture, LLM and agent workflow development, observability, telemetry, evaluation frameworks, guardrails, governance, reliability, cloud integration, and operational readiness. The role evaluates AI technologies, defines reusable patterns, partners with engineering and business stakeholders, supports scalable AI infrastructure, communicates technical decisions, and mentors engineers.

Design, develop, and deploy production AI/LLM applications and inference pipelines for a large enterprise Linux codebase. Integrate LLMs, vector databases, APIs, and RAG workflows; optimize model performance and scalability; implement monitoring, testing, and developer tooling; collaborate with engineers and researchers to productionize AI prototypes and modernize legacy C/C++ systems.

Build and operate secure, GPU-enabled Kubernetes and cloud infrastructure for AI/ML training and inference. Support data lakes, streaming analytics, model registries, model serving, infrastructure as code, CI/CD, monitoring, troubleshooting, and vulnerability remediation. Apply Zero Trust, NIST AI RMF, CMMC, and STIG requirements while collaborating with AI/ML and data platform teams in an Agile environment.

Architect and build Traba’s production AI agent platform, including orchestration, model strategy, retrieval, memory, tools, integrations, evaluation, observability, and deployment. Lead foundational technical decisions, customer-immersive solution design, 0-to-1 platform development, and hiring and mentoring of engineers. Partner with the CTO, product, and operations leadership on the long-term roadmap for autonomous workflows across industrial supply chain systems.

Build and lead Traba’s AI-powered platform for automating staffing and supply-chain workflows. Responsibilities include architecting scalable backend systems, real-time matching, autonomous vetting pipelines, APIs, data models, messaging infrastructure, and platform reliability. The role integrates AI agents and frontier models into production, drives architectural decisions, guides the roadmap, and establishes engineering standards across the stack.

Designs and maintains scalable data pipelines and architectures supporting AI and machine learning workloads for Army intelligence operations. The role develops data models, ensures data quality, availability, security, and governance, optimizes workflows for real-time analytics, integrates AI/ML solutions, and collaborates with internal and external stakeholders. Expertise with Palantir Foundry, programming, ETL, databases, and large language models is required, along with an active TS/SCI clearance and eligibility for TS/SCI with polygraph.

Designs and delivers AI-enabled and automation-driven workforce productivity solutions. Partners with business leaders to identify opportunities, translate needs into requirements and workflows, build prototypes and production solutions, establish success measures, and implement responsible AI governance. Uses enterprise AI and automation platforms to improve productivity, operations, quality, service delivery, and revenue outcomes. Requires independent cross-functional collaboration, strong business judgment, solution design expertise, and experience with AI architecture, workflow automation, and organizational adoption.

Supports AI and business operations improvements across departments by implementing ChatGPT Business and TrueContext, improving dashboards and digital forms, documenting workflows, identifying automation opportunities, and standardizing processes. The intern researches emerging technologies, creates process documentation and tools, collaborates cross-functionally, and owns practical projects within a manufacturing business.

Own the architecture and technical direction of Square’s go-to-market technology stack. Design, build, and operate production AI agents and automated workflows that improve sales productivity. Lead complex initiatives through architecture, implementation, testing, deployment, and operations; establish AI evaluation, observability, security, reliability, and human-oversight practices. Partner with Sales, Product, Data, and Engineering teams to translate business needs into scalable solutions, guide integrations and build-versus-buy decisions, and mentor engineers through influence and technical leadership.

Own the architecture and technical strategy for Square’s go-to-market systems. Build and operate production AI agents, automated workflows, and integrations that improve sales productivity. Lead complex initiatives through architecture, implementation, testing, deployment, and operations while establishing AI evaluation, observability, security, reliability, and human-oversight practices. Partner with Sales, Product, Data, and Engineering leaders to translate business needs into scalable solutions, make build-versus-buy decisions, mentor engineers, and improve business impact through data and user feedback.

Build and lead Crown Castle’s Data and AI Governance Engineering capability. Establish governance, data quality, metadata, lineage, access, observability, and AI governance strategies while automating controls and workflows across Databricks and the broader data ecosystem. Remain hands-on with prototyping, testing, troubleshooting, and reusable engineering patterns. Partner with technical, legal, security, risk, audit, architecture, and business teams to improve data trust, accountability, discoverability, and responsible AI adoption.

Lead cybersecurity GRC, privacy, and AI governance advisory engagements for enterprise and private equity clients. Design governance models, risk programs, control frameworks, privacy operations, third-party risk programs, and AI compliance frameworks aligned with NIST AI RMF, EU AI Act, and ISO 42001. Advise executives, develop board-ready deliverables, manage client workstreams, support business development, and mentor consulting teams.

Build and operate production-grade private AI platform capabilities, including agentic and multi-agent workflows, orchestration, evaluation, monitoring, fine-tuning, runtime optimization, safety controls, APIs, and rollback mechanisms. Partner with data and infrastructure teams to improve reliability, performance, and deployment quality. Senior engineers also mentor teammates, review designs, and lead major platform components.

Lead development of Avride’s ML infrastructure, initially owning GPU inference performance for onboard and simulation workloads. Build and optimize C++ execution frameworks, runtimes, and distributed infrastructure supporting neural network pipelines. Collaborate with applied ML teams on model architecture, diagnose performance issues, and expand ownership across broader ML pipelines. The role requires deep GPU and PyTorch knowledge, distributed systems experience, and strong expertise in multithreaded C++ environments.