Top Artificial Intelligence Jobs

Designs, builds, and operates Signature Aviation’s enterprise Azure AI platform, including AI gateways, agent frameworks, retrieval systems, evaluation, observability, secure infrastructure, and governed sandboxes. Works as a forward-deployed engineer with business and engineering teams to productionize AI use cases, then converts deployment lessons into reusable platform services, SDKs, templates, and standards. The role requires hands-on software, cloud, Kubernetes, security, networking, and reliability engineering expertise.

Build and maintain Java and Spring Boot backend services for the Compass open-banking platform. Integrate AWS Bedrock models, Knowledge Bases, and Agents to deliver RAG, summarization, and operator-assistance features. Develop ingestion pipelines for banking artifacts, OpenSearch indexing and vector retrieval, REST APIs, Thymeleaf administration views, and CI/CD workflows. Optimize security, scalability, and performance while collaborating with mappings, AI content, and frontend teams.

Lead technical presales and solution architecture for AI, cloud, and cybersecurity transformations. Design AI-ready hybrid and sovereign cloud infrastructures, governance frameworks, security controls, and modernization roadmaps. Build POCs, demos, pilots, infrastructure-as-code, DevOps pipelines, and SOC automation solutions. Develop proposals, SOWs, pricing models, reusable accelerators, and executive-facing thought leadership while enabling sales, engineering, security, and OEM partner teams.

Lead a small AI engineering team while remaining hands-on in research and production software development. Build agentic capabilities for monitoring, guardrailing, evaluation, collaboration, and security. Perform exploratory data analysis, feature engineering, model fine-tuning, prototyping, and technical design. Translate AI research into scalable product features, set technical direction, mentor engineers, and deliver reliable production code.

Hands-on engineer responsible for securing internally developed applications, APIs, AI-enabled solutions, and integrations. The role performs vulnerability assessments, source-code reviews, architecture assessments, penetration testing, and remediation validation. Responsibilities include strengthening authentication, authorization, API protections, CI/CD security testing, cloud application architectures, logging, and monitoring. The engineer also evaluates LLM applications and AI agents for prompt injection, data disclosure, insecure tool execution, and excessive permissions, while communicating risks and corrective actions to stakeholders.

Build production-ready agentic AI capabilities focused on monitoring, guardrails, evaluation, security, quality, and human-agent collaboration. Perform machine learning tasks including exploratory data analysis, feature engineering, and model fine-tuning. Translate research into scalable software, rapidly prototype features, collaborate cross-functionally, and explore emerging agentic technologies.

Design and maintain enterprise AI security and risk frameworks aligned with global regulations and standards. Translate requirements into policy-as-code, security baselines, technical controls, and continuous monitoring logic. Assess AI-specific threats such as prompt injection, data leakage, model extraction, shadow AI, and supply-chain vulnerabilities. Partner with AI/ML, infrastructure, security, Legal, Privacy, and assurance teams to embed governance into architectures, CI/CD workflows, and runtime environments. Own AI policy lifecycle, privacy safeguards, model adoption decisions, and cross-border data requirements.

Leads development of an AI-first, enterprise-scale loan origination and servicing platform using C#, .NET, SQL Server, modern UI frameworks, microservices, and event-driven architecture. Designs complex features, integrates AI capabilities and third-party systems, modernizes legacy applications, supports migration, optimizes performance, implements security and testing, troubleshoots distributed systems, and mentors engineers.

Own Compass’s AI product security roadmap and secure generative and agentic AI systems across models, RAG pipelines, tool invocation, and user-facing outputs. Responsibilities include threat modeling, production guardrails, prompt-injection and jailbreak defenses, retrieval security, AI supply-chain assessment, autonomous-agent controls, detection engineering, secure development tooling, and executive-level risk communication. The role requires production security engineering experience with AI/ML systems, cloud and application security, adversarial ML, red teaming, and strong Python or Go skills.

Owns Compass’s AI product security strategy and roadmap. Responsibilities include threat modeling AI features, securing RAG and vector-store retrieval, building LLM guardrails, mitigating prompt injection and jailbreaks, constraining autonomous agents, assessing AI supply-chain risks, developing abuse detection and telemetry, and automating secure-by-default libraries, CI/CD checks, and evaluation harnesses. The role also advises engineering teams and executives on AI security risks and remediation.

Own Compass’s AI product security strategy and roadmap. Threat model AI features, RAG pipelines, agentic workflows, and automated decisioning; build guardrails against prompt injection, jailbreaks, unsafe outputs, and data exfiltration; secure retrieval and AI supply chains; design autonomy controls; implement monitoring and detections; and automate secure development patterns through libraries, CI/CD checks, and evaluation harnesses. Advise engineering teams and executives on AI security risks and architecture.

Own the security roadmap for Compass’s generative and agentic AI products. Threat model AI features, RAG pipelines, multi-agent workflows, and automated decisioning; build protections against prompt injection, jailbreaks, unsafe tool use, and data exfiltration; secure retrieval layers and AI supply chains; develop monitoring, detections, CI/CD checks, libraries, and evaluation harnesses; and advise engineering teams and executives on AI risk.

Build and operationalize AI/ML threat detection systems for identity, network, device, and session telemetry. Develop real-time streaming pipelines, risk scoring and enforcement systems, MLOps platforms, and security controls for autonomous AI agents. Detect identity compromise, privilege escalation, data exfiltration, prompt injection, agent drift, and unauthorized access. Train, evaluate, deploy, and monitor production ML models while enabling autonomous investigation, containment, and remediation.

Designs, develops, tests, deploys, and improves AI agents and multi-step workflows for legal and business operations. Partners with attorneys and business teams to translate workflows into scalable solutions using Azure Databricks, Azure, n8n, LangGraph, and related technologies. Responsibilities include RAG, evaluation datasets, automated testing, observability, security, access controls, production deployment, troubleshooting, monitoring, and user adoption. Requires strong Python, SQL, AI engineering, enterprise integration, and production software development skills.

Secure AI, machine learning, inference, and developer platforms through threat modeling, architecture and code reviews, secure API and distributed-system design, model and supply-chain security, and security automation. Build controls for authentication, authorization, isolation, rate limiting, monitoring, anomaly detection, CI/CD scanning, and protection against prompt injection, model extraction, adversarial inputs, and data leakage. Partner closely with ML, platform, infrastructure, engineering leadership, and the CISO.

Designs, develops, deploys, and improves production AI agents and agentic workflows using Azure, LangGraph, n8n, Python, enterprise APIs, and governed data. Responsibilities include RAG, tool integrations, evaluation, testing, security, observability, deployment, troubleshooting, and continuous improvement. Partners with legal and business stakeholders to translate workflows into reliable AI solutions, while mentoring engineers, conducting code reviews, and establishing engineering standards.

Leads enterprise AI architecture and operationalization for federal mission environments. Designs scalable AI-as-a-Service platforms, multimodal and generative AI solutions, cloud-native architectures, MLOps pipelines, secure guardrails, data streaming ecosystems, and interoperable integrations. Guides modernization, governance, DevSecOps, and near-real-time analytical capabilities across AWS and Google Cloud. Provides technical leadership, architecture reviews, mentorship, proposal support, and executive communications.

Designs, develops, and operationalizes enterprise AI applications, agents, copilots, RAG pipelines, semantic search, and intelligent workflows. Integrates LLMs and AI platforms with enterprise, manufacturing, and data systems using APIs and orchestration frameworks. Establishes AI architecture standards, governance, security, observability, evaluation, and lifecycle practices while optimizing performance, reliability, adoption, and cost.

Develop and maintain scalable cloud-native web, mobile, API, and data products; migrate services to public cloud; implement CI/CD and test automation; contribute to Agile teams, troubleshoot production issues, and promote secure, high-quality software practices.

Drives enterprise AI adoption by leading Microsoft 365 Copilot training, workshops, office hours, and business-unit enablement. Helps users build simple agents with Copilot and Copilot Studio while ensuring governance, permissions, and data-handling compliance. Maintains AI resources, tracks adoption metrics, standardizes intake processes, supports responsible AI practices, and automates manual workflows. Requires strong communication, facilitation, problem-solving, relationship-building, and continuous-improvement skills.

Provides technical direction for AI threat defense within Cyber Defense. The role develops strategies, architectures, detection content, telemetry, investigation methods, playbooks, and response workflows for threats involving AI applications, agents, models, infrastructure, and integrations. It also embeds responsible AI into SOC and CIRT operations, partners with engineering and AppSec teams, tracks emerging adversary techniques, mentors security professionals, and influences executives, customers, regulators, and industry groups.

Lead AI application security across Fortinet by conducting threat modeling, code reviews, penetration testing, AI red teaming, and architectural assessments. Develop security guardrails, filters, policy controls, and CI/CD security checks for LLMs, chatbots, MCP implementations, and autonomous agents. Research emerging AI attack vectors, define mitigation strategies, coach teams, and ensure AI systems meet security, safety, privacy, and compliance standards.

Own security assessment and risk management for AI, LLM, Claude, MCP, and agentic systems. Define security controls, access governance, monitoring frameworks, standards, and training. Partner with AI Enablement and engineering teams on threat modeling, architecture reviews, and secure deployment. Build AI security monitoring tools, investigate risks such as prompt injection and data leakage, and track emerging attack vectors, defenses, and regulations.

Leads AI engineering teams in designing, productionizing, and integrating enterprise AI, generative AI, automation, and MLOps solutions. Drives rapid delivery, responsible AI adoption, observability, security, and cross-functional automation initiatives across business functions. Partners with technology, business, architecture, and security teams to deliver scalable solutions using cloud, enterprise platforms, agent-based systems, LLMs, and retrieval technologies.

Build production agentic AI systems and internal automation for business teams. Prioritize high-impact workflows, develop multi-step LLM agents, integrate platform capabilities, implement human-in-the-loop controls, and establish reusable design patterns. Evaluate reliability, accuracy, speed, and failure modes while collaborating with nontechnical stakeholders. The role requires strong Python and backend engineering skills, production LLM experience, systems thinking, and the ability to operate independently in ambiguous problem spaces.