Top Artificial Intelligence Jobs

Build and operate PEMCO’s end-to-end AI platform, including GPU infrastructure, model serving, AI gateways, retrieval systems, agent orchestration, observability, evaluation, cost telemetry, security, and governance. Lead production deployment, monitoring, incident response, lifecycle management, and architecture standards for AI systems. Optimize model routing, costs, performance, and reliability across Azure and potential on-premises environments while influencing organization-wide AI platform and governance decisions.

Build and operate Turing’s enterprise AI platform, including platform services, model gateways, multi-tenant infrastructure, CI/CD, infrastructure as code, deployment tooling, observability, and FinOps telemetry. The role supports reliable deployments across customer cloud accounts, dedicated instances, and sovereign environments. Responsibilities include production software engineering, on-call operations, incident response, secure platform design, and operational accountability.

Architects modular, multi-tenant SaaS platforms and AI-driven engineering workflows. Owns service boundaries, module contracts, identity, security, cloud infrastructure, migrations, observability, and agent guardrails. Designs AI product features, selects model providers, establishes evaluation and cost controls, and reviews high-risk changes. Coaches developers on specification writing and agent orchestration while collaborating with product and engineering leadership.

Designs and implements hardware and software security architectures for AI inference systems. Owns hardware root of trust, secure boot, confidential computing, isolation, embedded firmware security, model protection, and threat modeling. Leads security features from architecture and emulation through deployment and remediation, collaborating across hardware, software, and research teams. Requires extensive hardware security, silicon, embedded systems, low-level firmware, and security engineering experience.

Design, develop, and deploy production AI/ML systems involving foundation models, agentic workflows, RAG, multimodal learning, model evaluation, and edge inference. Build data pipelines, cloud architectures, APIs, and software integrations while applying AI assurance practices. Lead technical tasks, mentor junior staff, collaborate with customers and cross-functional teams, contribute to Agile delivery, and support advanced government and commercial R&D proposals.

Develops AI-powered cybersecurity applications that protect enterprise systems, applications, and data. Responsibilities include integrating and maintaining threat defense and user access management capabilities, troubleshooting escalated incidents, investigating security issues, coordinating with managed security providers, and recommending corrective actions. The role involves cloud-native development, agentic AI, RAG, security controls, APIs, testing, monitoring, and software engineering best practices.

Build autonomous AI agents that translate proven attack paths into targeted security-control changes, safely apply or stage those changes, and verify remediation by rerunning attacks. Responsibilities include ML reasoning systems, evaluation and training data pipelines, counterfactual experiments, heterogeneous control-plane integrations, safety architecture, staged rollouts, rollback, auditability, and diagnosing agent failure modes. The role requires production ML engineering, agentic systems or LLM post-training, outcome-based evaluation, Python software engineering, distributed systems, and cloud infrastructure experience.

Build and operate production AI and LLM systems for safety-critical enterprise applications. Responsibilities include multi-step agent orchestration, retrieval across Neo4j and vector stores, evaluation frameworks, model comparison, observability, quality monitoring, failure handling, and production incident response. The role requires strong Python and experience with asynchronous services, LLM workflows, evaluation, retrieval, graph reasoning, and enterprise AI security. It also contributes to model selection, technical direction, and the AI roadmap.

Designs, builds, secures, and governs enterprise-scale agentic AI systems. Responsibilities include developing LLM, RAG, prompt engineering, and multi-agent solutions; implementing defenses against prompt injection, jailbreaking, and tool misuse; creating resilient, observable, auditable pipelines; and applying software engineering, CI/CD, infrastructure-as-code, and microservices practices. The role also leads cross-functional alignment, advises executives, develops architecture roadmaps, and supports AI governance in regulated environments.

Lead AI and LLM offensive security engineering by breaking agentic systems, researching prompt injection, tool-use exploitation, model and supply-chain attacks, and automating repeatable attack modules for NodeZero. Build production-grade LLM applications and microservices with safety, reliability, cost, and observability considerations. Drive threat modeling, exploit development, and technical strategy for AI security capabilities.

Build and maintain scalable .NET applications across web, desktop, console, and mobile platforms while developing AI capabilities on Azure. Responsibilities include creating RAG solutions, LLM agents, MCP integrations, AI evaluation harnesses, guardrails, and secure application integrations. The role also supports databases, automated testing, DevOps practices, and third-party APIs. The engineer will mentor developers on GitHub Copilot, Claude, AI coding practices, and responsible review of AI-generated code.

Own technical presales pilots for Commure Ambient AI across healthcare practices and ambulatory EHRs. Scope workflows, build and debug browser, DOM, and API integrations, configure advanced AI features, coordinate templates, triage issues, maintain Salesforce status, support RFPs and security questionnaires, and deliver documented handoffs to implementation. Partner with account executives, engineering, template, and operations teams while improving pilot tooling and processes.

Build and operate production AI agents, including autonomous coding systems, retrieval and knowledge layers, MCP tooling, evaluation frameworks, and secure LLM workflows. Improve model cost, latency, reliability, and observability on AWS Bedrock while integrating Snowflake, documentation, knowledge graphs, Jira, Slack, and developer workflows. Responsibilities include prompt and model evaluation, security hardening, design documentation, code review, mentoring, and on-call ownership.

Leads strategy, requirements, roadmap, lifecycle, commercialization, adoption, and economics for AI-enabled regulated medical products. Defines intended use, claims, change-control plans, and portfolio investments while partnering with clinical, regulatory, quality, commercial, and technical teams. Oversees AI/ML model governance, product-market validation, workflow integration, analytics, evidence generation, and post-market performance for products developed under formal medical-device design controls.

Lead strategy, roadmap, and delivery for enterprise platform products enabling reusable rules, workflows, decisioning, approvals, and orchestration. Drive cross-functional alignment with engineering, architecture, AI Delivery, and security, measure platform adoption and outcomes, reduce technical debt, and build a high-performing platform product team while establishing scalable, AI-enabled product management practices.

Own AI product strategy for public sector and national security customers. Translate customer needs into requirements, roadmaps, pilots, and measurable outcomes across AI evaluation, red-teaming, runtime controls, governance, observability, and agentic systems. Partner with engineering, research, delivery, design, and go-to-market teams while analyzing model behavior, evaluation results, datasets, and customer workflows. The role is customer-facing, requires regular travel and on-site engagement, and focuses on high-consequence government environments.

Designs, develops, deploys, and supports generative AI applications on AWS using Amazon Bedrock. Builds RAG systems, AI agents, document-processing workflows, Python APIs, evaluation pipelines, and security safeguards. Integrates enterprise data and APIs, automates infrastructure and delivery, monitors production workloads, and optimizes performance, reliability, and cost. Translates customer requirements into solution designs, demonstrations, implementation plans, and operational handoffs.

Owns product strategy and roadmap for AI evaluation, guardrails, agent governance, and observability solutions serving government and national security customers. Conducts customer discovery, translates mission needs into requirements, leads prototyping and pilots, analyzes AI system behavior, partners with engineering and research teams, and creates customer-facing materials. The role is highly customer-facing, requires regular onsite engagement and travel, and supports high-consequence public sector deployments.

Designs enterprise AI architecture, standards, governance, and scalable production solutions. Leads generative AI, agentic systems, RAG, machine learning, Azure AI platform adoption, enterprise integrations, security, MLOps/LLMOps, and cost governance. Partners with business, technology, cybersecurity, data, and globally distributed Agile teams to deliver responsible AI solutions from concept through production.

Leads enterprise AI solution strategy, architecture, pre-sales, and implementation delivery for customers across the Americas. Defines AI use cases, validates feasibility, designs scalable architectures, guides Professional Services teams, and resolves complex technical challenges. Develops AI service offerings, methodologies, reusable assets, and enablement programs while mentoring architects, consultants, and project leaders. Requires deep experience with enterprise transformations, LLMs, multi-agent systems, generative AI, data architecture, cloud platforms, and production AI deployments.

Build and productionize AI agents for PR and communications workflows. Develop Python agent runtimes, MCP servers, Django/Vue applications, chat interfaces, streaming responses, permission-aware APIs, evaluation systems, and quality improvements. Collaborate on prompting, tool use, memory, guardrails, observability, security, deployment, and platform architecture while using customer feedback to improve agent quality, latency, and cost.

Leads CCC’s enterprise-wide architecture vision across AI, data, cloud platforms, SaaS products, APIs, integrations, and engineering practices. Guides AI-native platform development, data architecture, modernization, scalability, security, responsible AI, and operational resilience. Establishes architecture principles, governance, reusable patterns, and technical roadmaps while mentoring senior technical leaders. Partners with executives, Product, Engineering, Data Science, Security, Legal, and Compliance to balance innovation, reliability, customer trust, and business outcomes.

Conduct adversarial red teaming of large language models, multimodal models, and agentic systems. Identify vulnerabilities such as jailbreaks, prompt injection, tool-use abuse, and model extraction; automate attack generation and evaluation; measure guardrail robustness; use interpretability methods to locate failure modes; and produce reproducible attacks, evaluations, and technical reports or papers.

Lead the architecture and technical evolution of AI-native applications, including agentic systems, RAG pipelines, LLM infrastructure, data pipelines, observability, security, and developer tooling. Establish scalable cloud-native patterns, governance, API standards, and compliance controls while partnering with product, engineering, and research teams. Mentor senior engineers, evaluate emerging AI technologies, and guide production-grade LLM application development across enterprise platforms.

Technical authority for partner-facing Workday AI platform and integration architecture. Design reference architectures, agent orchestration and governance, and integration patterns. Enable GSI technical teams via deep technical training, demo readiness, code accelerators, and playbooks. Translate roadmap into partner implications and represent Workday in joint technical forums while coordinating with Forward Deployed and Solution Architecture teams.