Top Artificial Intelligence Jobs

Build and operate production agent infrastructure, including agent harnesses, sandboxed execution environments, context systems, observability, evaluation suites, and model-routing systems. Work directly with customers to turn prototypes into reliable products, then use production traces to improve environments, reward signals, and models. Own technical decisions across agent reliability, cost, latency, security, and scale in a founding startup role.

Build and support a production conversational AI system for fleet analytics. Responsibilities include extending LangGraph agents and domain tools, improving RAG retrieval, supporting production incidents, optimizing LLM cost and latency, developing evaluations and testing gates, and strengthening tenant isolation, authorization, and prompt-injection defenses. The role requires strong Python, PostgreSQL, LLM orchestration, observability, testing, and production-service experience, with Azure, vector search, guardrails, and frontend skills preferred.

Leads enterprise AI adoption, support, administration, training, governance, and risk management. Administers platforms such as Copilot and Claude, manages licensing and integrations, develops policies and guidance, partners on AI-enabled engineering workflows, pilots automations, monitors usage and costs, and supervises or mentors support staff.

Lead AI security research by designing and executing attacks against LLMs, RAG systems, multimodal models, and agentic applications. Build scalable red-teaming and evaluation tooling, investigate prompt injection, jailbreaks, data leakage, model attacks, and tool abuse, and translate findings into practical mitigations. Partner with engineering, product, Responsible AI, and detection teams; evaluate defenses, map threats to MITRE ATLAS and OWASP frameworks, brief executives, publish technical reports, mentor researchers, and shape AI security methodology.

Secures CI/CD pipelines, cloud infrastructure, software supply chains, and MLOps workflows. Builds controls for AI and LLM security, threat models AI systems, and develops internal AI agents for security automation. Partners with engineering teams on secure development practices, manages secrets and Kubernetes hardening, supports incident response, and reports on DevSecOps maturity and AI security posture.

Build reliable autonomous AI agents for identity operations and security. Responsibilities include developing agent harnesses, integrating OpenAI and Anthropic models, creating evaluation datasets and replay tests, measuring model quality and safety, protecting tenant boundaries, and improving prompts, retrieval, tools, and model routing. The role requires strong Python engineering, production LLM application experience, agent workflow or evaluation expertise, and ownership across development, release, and customer use.

Build reliable autonomous AI agents for identity operations and security. Develop agent harnesses for planning, tool use, memory, recovery, authorization, and workflow execution. Integrate and evaluate OpenAI and Anthropic models using quality, latency, cost, and safety metrics. Create evaluation datasets, replay tests, regression tests, and protections against prompt injection and unsafe actions. Own production improvements across code, evaluation, release, and customer use.

Secure the design, development, deployment, and operation of enterprise AI solutions. Evaluate AI platforms, machine learning models, and AI-enabled applications for cybersecurity, privacy, compliance, and third-party risks. Conduct threat modeling, architecture reviews, and security assessments; recommend controls for data protection, model security, identity, access, governance, and supply-chain security. Contribute to AI security strategy, governance, standards, and roadmaps while educating stakeholders and promoting secure-by-design practices.

Conduct applied, production-facing AI research to improve enterprise agents. Own fine-tuning, preference optimization, post-training pipelines, evaluation suites, and golden datasets. Analyze production traces and failures to create training data and targeted improvements. Collaborate on model routing, distillation, inference cost, and latency while determining when fine-tuning outperforms prompting, retrieval, or scaffolding.

Build reliable autonomous AI agents for identity operations and security. Responsibilities include developing agent harnesses for planning, tool use, memory, recovery, model routing, authorization, evaluation, and long-running workflows. Integrate OpenAI and Anthropic APIs, measure quality and safety, create replay tests, investigate failures, protect tenant boundaries, and improve prompts, retrieval, tools, and models. The Principal Engineer will set product engineering direction and develop shared components for production AI systems.

Serves as PDW’s senior AI/ML technical leader, defining enterprise AI strategy, architecture, standards, and roadmap while building production systems. Develops RAG solutions, AI agents, orchestration platforms, evaluation frameworks, APIs, data pipelines, and MLOps infrastructure. Leads AI security and governance, optimizes models across cloud and GPU environments, partners across engineering and business teams, and mentors teams adopting AI technologies. Requires extensive hands-on experience with generative AI, production ML systems, secure architecture, and enterprise deployment.

Leads the strategy, architecture, development, integration, and deployment of AI/ML capabilities for enterprise cybersecurity operations. Oversees LLM, agentic AI, RAG, threat hunting, vulnerability management, incident response, and defensive automation initiatives. Advises executives, directs technical roadmaps, leads multidisciplinary engineering teams, integrates AI with security platforms, and establishes secure production processes for classified environments. The role also drives governance, responsible AI, operational metrics, mentoring, and mission-focused delivery.

Own the architecture and research direction of AI models for earth observation and remote sensing data. Develop and evaluate novel approaches, define success metrics, scale experiments to petabyte-level training, integrate models with agent-based products, plan research roadmaps, and publish findings. The role requires strong AI research expertise, experience shipping AI systems, and comfort modeling non-language data such as images, time series, and sensor data.

Develop and maintain AI platform infrastructure, APIs, microservices, and data-processing systems supporting model and agent deployment. Collaborate with AI/ML teams and cross-functional partners to deliver scalable, reliable solutions. Troubleshoot performance and integration issues, contribute to architecture, and support production systems in a continuous-deployment DevOps environment.

Lead development and standardization of EHS programs across Micron's US expansion sites. Manage communications, documentation, training, data dashboards, construction medical clinic oversight, and workers' compensation. Partner with EHS leaders, construction, medical providers, and stakeholders to drive compliance, continuous improvement, and visibility into EHS performance.

Own defined product features and workflows from discovery through launch and post-launch improvement. Create product artifacts, partner with engineering and design, incorporate customer feedback and analytics, manage roadmaps and backlogs, prepare launch plans, and monitor adoption and quality. The role requires cross-functional collaboration, benefits administration domain learning, responsible use of AI-enabled tools, and hands-on technical engagement with engineers, including troubleshooting and scripting for production support.

Own the strategy, lifecycle, and performance of AI-enabled contact center products at Barclays. Lead cross-functional teams, prioritize backlogs, define user stories, manage risk and controls, and deliver customer-focused agent and servicing experiences. Apply AI, LLMs, automation, journey mapping, personalization, and data-driven insights to modernize contact center tools. Establish OKRs and KPIs while guiding product development from concept through launch and advising senior stakeholders.

Build and scale customer-facing Android marketplace applications using Kotlin, Compose, and modern Android architecture. Own complex features end to end, lead technical direction, improve testing and CI/CD, support releases and observability, and modernize legacy codebases. Use AI tools for coding, testing, debugging, documentation, and operational efficiency while mentoring engineers and collaborating with product, design, backend, platform, QA, and iOS teams.

Own enterprise AI architecture and strategy for retail use cases, designing production AI, ML, GenAI, MLOps, observability, security, and governance standards. Lead open-source framework adoption, AI solution architecture, model monitoring, threat modeling, productivity-tool governance, and build-versus-buy decisions. Partner with engineering, platform, security, legal, and business teams to deliver scalable solutions across merchandising, supply chain, stores, marketing, and e-commerce.

Build and operate a secure, locally hosted LLM platform for enterprise engineering and business use. Responsibilities include model evaluation and optimization, inference serving, RAG and search pipelines, permission-aware retrieval, security controls, evaluation frameworks, observability, safe agent tooling, integrations, CI/CD, reliability, and lifecycle management. The role leads architecture and roadmap decisions while partnering cross-functionally to deliver measurable, production-ready generative AI capabilities.

Design and build Docker’s AI Gateway, including model and tool routing, MCP services, control-plane infrastructure, secure credential management, policy enforcement, observability, caching, rate limiting, and agent evaluation systems. Lead architectural decisions, optimize performance and reliability at scale, guide roadmap planning, and mentor engineers while creating developer-facing APIs for AI applications.

Lead architecture and development of Netskope’s secure AI Gateway, building scalable, high-performance systems that route, inspect, transform, and secure LLM and API traffic. Responsibilities include designing distributed microservices, threat detection, dynamic multi-provider orchestration, credential management, DLP integration, and real-time content analysis. The role provides technical leadership, mentors engineers, partners with product teams, conducts reviews, and guides global engineering collaboration.

Lead the architecture and implementation of Docker’s AI Gateway, including model and tool routing, MCP, control-plane systems, credential vaulting, policy enforcement, observability, analytics, caching, and rate limiting. Solve complex distributed-systems problems, establish technical direction, improve infrastructure, mentor engineers, and shape product strategy. Build secure, scalable services supporting model orchestration, agent evaluation, replay, and enterprise-grade governance.

Lead enterprise architecture for AI, Generative AI, agentic systems, and intelligent platforms. Define target-state architectures, governance standards, reusable patterns, risk controls, data and context services, model lifecycle frameworks, and enterprise roadmaps. Guide platform and model choices across Microsoft and third-party technologies while embedding security, privacy, resilience, explainability, regulatory compliance, monitoring, and responsible AI controls. Partner with executives and cross-functional teams to govern AI adoption across the enterprise.

Build and operate production-grade AI agents that automate enterprise workflows and make bounded decisions. Design multi-agent architectures, RAG pipelines, tool integrations, governance controls, evaluations, monitoring, and secure deployments across business systems. Collaborate with technical and business stakeholders to identify automation opportunities, validate outcomes, manage risk, and improve AI solutions. Mentor engineers and develop reusable standards, documentation, and enablement materials.