Top Artificial Intelligence Jobs

Architects enterprise data warehouse and orchestration platforms, establishes data governance and semantic layers, and builds production RAG, LLM, and MLOps systems. The role also develops machine learning models for portfolio construction, evaluates AI tools, supports security audits, manages Python package publishing, and coordinates technical projects with stakeholders.

Define and lead AI security strategies and standards across data, models, APIs, integrations, and third-party use. Identify AI-specific risks, establish security requirements, and operationalize governance through threat modeling, control validation, monitoring, and regulatory alignment. Partner with senior engineering, risk, business, and technology stakeholders to secure cloud-based AI workloads, assess controls, communicate security trends, and drive consensus on complex issues.

Deploy and support industrial AI and digital manufacturing solutions across manufacturing sites. Translate plant needs into system requirements, coordinate implementation partners, manage data and equipment readiness, integrate AI systems with SAP, MES, EWM, and factory equipment, execute testing and deployment, and provide operational handover and troubleshooting. The role supports machine vision inspection, digital twins, edge computing, performance monitoring, and ongoing model or configuration updates.

Build and deploy autonomous and semi-autonomous AI agents for an Agentic SOC, integrating SIEM, EDR, XDR, and threat intelligence systems. Translate SOC playbooks into agentic pipelines, optimize RAG workflows and LLM performance, and design human-in-the-loop escalation. Secure LLM architectures against prompt injection, data poisoning, tool abuse, and OWASP Top 10 for LLM risks. Develop scalable AI security solutions using cloud platforms and collaborate with analysts and threat hunters.

Build and deploy AI agents for security operations, translating SOC playbooks into agentic pipelines and optimizing RAG and LLM workflows. Design human-in-the-loop escalation, integrate SIEM, EDR, XDR, and threat intelligence systems, and secure AI architectures against prompt injection, data poisoning, and tool abuse. The role also involves production cloud deployment, orchestration, vector databases, and scalable AI services.

Design, build, deploy, and maintain production-grade generative AI solutions using LLMs, transformers, RAG pipelines, and agentic systems. Develop prompts, evaluation frameworks, safety guardrails, and multi-step workflows integrating tools and APIs. Deploy and monitor AI applications across cloud environments while optimizing accuracy, cost, latency, reliability, and safety. Collaborate with Product, Engineering, and ML Ops to move prototypes into scalable production systems.

Lead the technical direction, development, evaluation, security, observability, and cost optimization of production LLM agent systems. Build shared agent infrastructure, improve reliability and fault tolerance, establish testing and rollout standards, and partner with engineering, product, research, and security teams. Mentor engineers, support AI coding-agent adoption, investigate incidents, and guide model selection and production readiness across teams.

Designs and operates governed AI-assisted cybersecurity workflows for triaging code, configurations, scan results, vulnerabilities, and other security artifacts. Builds Python services, APIs, retrieval pipelines, prompts, and evaluation tools; integrates deterministic security tools with human validation; measures quality, drift, cost, and analyst efficiency; and enforces access controls, provenance, auditability, redaction, and prompt-injection defenses. Supports continuous assessment of federal internet-facing assets and produces actionable remediation guidance.

Supports the design, configuration, testing, evaluation, monitoring, and governance of applied AI, generative AI, machine learning, and agentic workflow solutions in banking. Develops prompts, RAG pipelines, evaluations, structured outputs, and workflow controls while supporting Microsoft Foundry, Copilot, Snowflake, vendor reviews, AI intake documentation, privacy, cybersecurity, and regulatory oversight. Partners with AI Scientists, engineering, risk, compliance, and business teams to deliver controlled human-in-the-loop solutions.

Leads Ludia Consulting’s AI strategy, governance, security, budget, and engineering team. Builds production AI agents, copilots, reusable accelerators, RAG pipelines, and responsible-AI controls using Microsoft technologies, Dynamics 365, Power Platform, and Dataverse. Oversees customer AI implementations, adoption, enablement, and delivery standards while partnering with executives, sales, and business functions. The role requires hands-on coding, technical architecture, team leadership, customer engagement, mentoring, and measurable commercialization of AI solutions.

Develop threat models, kill chains, security levels, and failure-mode libraries for potentially adversarial coding agents. Design realistic attacks, adjudicate flagged agent behavior, red-team Watcher monitors, and build adversarial test suites. The role requires hands-on security experience, code and infrastructure understanding, technical threat modeling, strong research and writing skills, and comfort working independently on ambiguous problems.

Own AIUC’s security function as the founding security lead. Develop and evolve AI agent security standards, address emerging threats, build security infrastructure and organizational controls, establish policies and incident response, advise enterprise clients, and represent AIUC externally. The role combines strategic, technical, operational, and client-facing responsibilities, requiring deep security expertise, strong communication skills, and knowledge of AI vendor security evaluation.

Build and operate production-grade agentic AI systems for enterprise SaaS, including orchestration, MCP tools, memory, RAG, knowledge retrieval, security guardrails, evaluation, and observability. Own products from prototyping through production, make tradeoffs among accuracy, latency, reliability, and cost, support incident response, create reusable tooling, and mentor engineers. The role requires expert Python, cloud-native infrastructure, distributed systems, and hands-on LLM production experience.

Build and lead production AI engineering solutions for client engagements, including agentic systems, RAG pipelines, vector retrieval, MCP integrations, evaluations, observability, and guardrails. Partner with architects and consultants, coach client engineering teams, and take AI systems from prototype through monitored production deployment. The role also contributes to internal AI products and reference architectures.

Build and deploy production AI features including LLM workflows, RAG systems, agents, classification, extraction, and machine learning models. Own ingestion, embeddings, indexing, retrieval, evaluation, monitoring, API integration, responsible AI, and production reliability. Collaborate with product, design, data, platform, and client stakeholders; explain technical tradeoffs clearly. Contribute to reusable AI standards and architectures while rapidly prototyping and hardening successful solutions.

Develop, test, and optimize prompts for generative AI models and large language models. Analyze prompt performance, troubleshoot model behavior, and collaborate on AI solutions that improve mission workflows. Support model fine-tuning and Retrieval-Augmented Generation initiatives while researching advances in AI. The role includes structured prompt-engineering training before transitioning to client-facing work supporting U.S. Customs and Border Protection.

Develop and optimize prompts for generative AI and large language models supporting U.S. Customs and Border Protection. Test prompt variations, analyze results, troubleshoot model behavior, and collaborate on fine-tuning and Retrieval-Augmented Generation solutions. The role requires software development experience, programming skills, onsite availability in Ashburn or Washington, D.C., and the ability to pass a CBP background investigation.

Develop and optimize prompts for large language models and generative AI applications supporting U.S. Customs and Border Protection. Responsibilities include testing prompt variations, analyzing model behavior, collaborating on process automation, supporting fine-tuning and RAG strategies, researching AI advancements, and debugging model issues. The role requires onsite work in Ashburn, Virginia and/or Washington, D.C., U.S. citizenship, and successful completion of a CBP background investigation.

Develop and optimize prompts for generative AI models and LLMs supporting U.S. Customs and Border Protection. Test prompt variations, analyze model behavior, support fine-tuning and RAG strategies, troubleshoot issues, and collaborate with technical teams to integrate AI solutions. The role requires software development experience, hands-on LLM experience, programming skills, and onsite availability in Ashburn, Virginia and/or Washington, D.C.

Develops and optimizes prompts for generative AI models and large language models supporting U.S. Customs and Border Protection. Responsibilities include prompt testing, performance analysis, model behavior debugging, collaboration on AI process integrations, research into emerging techniques, and support for fine-tuning and RAG strategies. The role requires programming experience, hands-on LLM experience, prompt engineering knowledge, onsite availability in Ashburn or Washington, D.C., U.S. citizenship, and ability to pass a CBP background investigation.

Design, build, and operate production AI agents and foundational cloud platform infrastructure. Develop agentic workflows for incident triage, environment provisioning, configuration generation, remediation, and developer self-service. Operate highly available AWS and Kubernetes systems, evolve infrastructure-as-code and GitOps practices, improve observability and reliability, and contribute to Cortex’s AI-augmented developer platform. Mentor engineers, document architecture, participate in on-call rotations, and collaborate across engineering, product, and security teams.

Develops and optimizes prompts for generative AI and large language models supporting U.S. Customs and Border Protection. Responsibilities include testing prompt variations, analyzing model behavior, collaborating on AI integrations, researching emerging techniques, supporting fine-tuning and retrieval-augmented generation, and debugging model issues. The role requires software development experience, hands-on LLM experience, programming knowledge, prompt engineering familiarity, U.S. citizenship, and onsite availability in Ashburn or Washington, D.C.

Design and deploy production AI/ML systems for federal cybercrime investigations. Build machine learning models, data pipelines, ETL workflows, databases, APIs, and microservices for investigative analytics. Implement explainable, auditable, secure, and compliant AI with human oversight, monitoring, bias detection, and model governance. Support authorization and risk management processes, document architectures and data provenance, and collaborate with engineering, infrastructure, cybersecurity, and investigative stakeholders.

Serves as a technical architect and strategic advisor for Cisco’s enterprise AI strategy. Defines architectures for agentic systems, multi-agent orchestration, durable execution, and interoperable AI platforms. Leads build-versus-buy decisions, establishes engineering standards, mentors senior technical staff, resolves complex AI challenges, and owns security for autonomous systems. Requires extensive distributed systems, LLM production, and multi-agent experience, with preferred expertise in AIOps, inference optimization, AI governance, and executive-level technical influence.

Lead the architecture and development of an internal AI developer platform. Build cross-platform onboarding, telemetry, secure sandboxing, AI agent integrations, and provider-agnostic abstractions. Establish engineering standards, review code and architecture, support production incidents, evaluate emerging AI technologies, and shape the platform roadmap. Mentor senior engineers and collaborate with platform users and stakeholders in a regulated financial-services environment, with emphasis on security, observability, reliability, and agent evaluation.