Top AI Engineer Jobs

Lead the architecture and development of Cisco’s enterprise AI defense platforms, including control planes, data planes, policy engines, and security services. Build high-throughput Go and Python microservices, secure agentic AI and MCP workflows, and implement protections against prompt injection, jailbreaks, tool abuse, and data exfiltration. Drive zero-trust architecture, cross-functional technical strategy, production delivery, engineering standards, and mentorship across security research, product, and platform teams.

Support the design, development, testing, and deployment of AI-powered applications across front-end, back-end, and data workflows. Collaborate with engineers, researchers, product managers, and operators to translate customer needs into product requirements, integrate AI models, evaluate outputs, improve reliability and usability, and ship production-grade tools for government, healthcare, and industrial customers.

AI/ML engineering intern will help design, build, evaluate, and deploy LLM-powered applications and machine learning systems. Responsibilities include developing data pipelines, working with structured and unstructured data, supporting model training and optimization, preparing systems for production monitoring, and collaborating with engineers, researchers, and product teams. The role offers mentorship and hands-on experience applying AI to healthcare, government, and energy workflows.

Interns build backend services, APIs, distributed systems, platform components, developer tools, and AI/ML infrastructure. Responsibilities span design, implementation, testing, deployment, maintenance, reliability, performance optimization, observability, and collaboration with Product, ML, Infrastructure, and customer-facing teams. The role is intended for undergraduate juniors and seniors graduating by June 2028 and offers mentorship through production-focused engineering work.

Define Horizon3’s long-term AI/LLM offensive security strategy and lead development of scalable autonomous attack capabilities in NodeZero. Direct research and productization for prompt injection, tool exploitation, data exfiltration, privilege escalation, model and supply-chain attacks, and agentic systems. Establish architectures, threat models, safety and reliability standards, and evaluation practices for LLM-powered offensive applications. Influence company-wide technical decisions, mentor senior engineers, and represent the company through research and industry leadership.

Design and build enterprise AI platform infrastructure, including LLM gateways, MCP registries, abstraction APIs, AI agents, automation, governance, observability, and compliance tooling. Evaluate AI models and emerging frameworks while balancing cost, safety, and performance. Lead technical design for cross-functional projects and support engineers through office hours, Slack, and adoption programs. Build and operate production backend services using Python or TypeScript, cloud platforms, containers, APIs, and enterprise security patterns.

Own technical sales engagements for AI-powered customer experience assurance solutions, from discovery through technical win. Lead CX and AI stack discovery, solution design, tailored demos, workshops, proofs of concept, and executive business cases. Partner with Sales, Product, and Implementation teams on RFPs and handoffs, while creating enablement materials and mentoring teammates on agentic AI and prompt engineering.

Develop and optimize compiler tools targeting Intel CPUs, GPUs, and accelerators. Responsibilities include adding compiler features, integrating processor capabilities, maintaining LLVM, Clang, SYCL, and Fortran frameworks, debugging, performance analysis, vectorization, parallelization, benchmarking, and open-source collaboration. The role supports AI and high-performance computing applications and involves cross-functional work with hardware, validation, and global engineering teams.

Designs, builds, and operates AWS-based data lakes and GenAI platforms for safety and mission assurance. Develops heterogeneous data pipelines, RAG and agentic workflows, APIs and MCP servers, identity integrations, evaluation and observability systems, and governance controls. Integrates AI capabilities with enterprise applications and Microsoft 365 while ensuring security, traceability, responsible AI, and regulatory compliance. Requires collaboration with technical and mission stakeholders and residence in the Greater Cleveland Area.

Supports the development and testing of AI agents, prompt engineering, integration code, MCP server components, and AI plugins. Reviews model outputs, writes unit tests, participates in code reviews and CI/CD workflows, and contributes to documentation and sprint ceremonies. This onsite rotational role provides mentorship and hands-on exposure to production AI engineering systems.

Develop visual reasoning, 3D spatial understanding, and embodied intelligence capabilities for robotics foundation models. Design scalable robotics data pipelines covering collection, teleoperation, annotation, quality assessment, and evaluation. Research multimodal foundation model approaches for robotic planning, reasoning, and decision-making, including VLA architectures. Collaborate with foundation model researchers and robotics partners to evaluate capabilities and address real-world deployment challenges.

Develop, test, deploy, and maintain deep learning computer vision models for space and missile warning applications. Build sensor fusion and target tracking solutions, optimize real-time systems with GPU programming, and contribute to embedded systems and fire control architectures using Python, C, C++, and Rust. Collaborate with multidisciplinary teams to deliver mission-critical AI capabilities.

Design, implement, and ensure quality of systems stack, tools, and SDK integration for AI frameworks. Collaborate with hardware and ML teams, apply engineering principles, write low-level/high-performance code, define architecture, estimate scope, and drive coding standards.

Build and deploy production agentic workflows that transform recurring business processes across company functions. Responsibilities include mapping workflows, evaluating AI opportunities, developing agents on a shared framework, defining approval gates, improving reliability and evaluation, preparing data and systems for agent use, training functional teams, and documenting reusable implementation patterns. The role requires close collaboration with non-engineers, senior stakeholders, and domain experts, plus strong ownership through handoff and ongoing platform improvement.

Lead architecture and delivery of scalable AI infrastructure, including model serving, feature stores, training systems, and ML observability. Partner with ML, data, and AI teams to improve workflows and reliability. Manage AWS CPU/GPU resources, establish monitoring standards, shape technical strategy, and mentor engineers. The role requires deep expertise in distributed systems, production ML workflows, search systems, vector databases, and modern ML infrastructure tooling.

Lead design and delivery of cloud-native AI platforms and AIOps solutions. Drive PoCs, scale LLM/RAG/agent architectures across GCP and Azure, mentor engineers, embed AI governance, integrate security and CI/CD, and align roadmaps to business outcomes.

Develop and deploy production-grade 3D LiDAR and computer-vision perception systems for mobile robots. Own algorithms from sensor characterization through fleet deployment, while building AI agents and automated pipelines for data labeling, dataset curation, edge-case triage, and regression validation. Collaborate across controls and software teams, lead architecture and testing efforts, mentor engineers, and improve collision avoidance and autonomous behaviors across diverse operating environments.

Designs, builds, and scales enterprise GenAI and multi-agent systems using ADK and LangChain. Responsibilities include agent orchestration, tool chaining, guardrails, API and data-platform integration, real-time AI pipelines, observability, performance optimization, and production deployment. The role partners with Risk, Compliance, and Model Governance teams to meet regulatory requirements, while providing technical leadership and mentorship across AI engineering teams in a financial-services environment.

Design, build, and scale enterprise LLM-powered and agent-based systems using ADK and LangChain. Develop multi-agent orchestration, guardrails, API integrations, real-time pipelines, observability, and production deployment workflows. Partner with Risk, Compliance, and Model Governance teams on regulatory controls and validation. Provide technical leadership, mentorship, and engineering standards while optimizing AI system performance in a regulated financial-services environment.

Designs, builds, integrates, and optimizes AI solutions within the Salesforce ecosystem using Einstein, Agentforce, Data Cloud, CRM Analytics, Flow, and Apex. Develops AI workflows, prompt templates, conversational agents, integrations, and governance practices. Collaborates with stakeholders and technical teams to identify AI opportunities, troubleshoot solutions, support responsible AI, monitor performance, and improve adoption. Requires Salesforce expertise, AI experience, a bachelor’s degree, and Salesforce certifications.

Designs and develops scalable enterprise data architectures, models, integration strategies, governance practices, and cloud data platforms. Builds optimized data pipelines for analytics, AI, and machine learning workloads, including preprocessing, feature engineering, real-time inference, and production model integration. Collaborates with architects, engineers, analysts, stakeholders, and distributed teams across client projects. The role is hybrid in Leawood, Kansas, with occasional client-site travel.

Develop and integrate AI/ML capabilities for classified Battlespace C2, threat intelligence, data extraction, modeling, dashboards, and threat inject generation. Configure virtualized test environments, execute cybersecurity scans, maintain secure baselines, support RMF and ATO compliance, and perform integration and regression testing. Create technical documentation, track defects and configuration changes, and collaborate with software, systems, cybersecurity, and government stakeholders. The role requires Secret clearance with Top Secret/SCI eligibility and 5% travel.

Lead the architecture, development, evaluation, and operation of enterprise autonomous AI agents integrated with Salesforce. Build stateful multi-agent workflows using LangGraph, Claude, and MCP; implement evaluation, observability, security, and PII protection; optimize model routing and performance; and establish engineering standards, API contracts, and architecture roadmaps. The role requires extensive software engineering and production LLM experience, strong debugging and stakeholder communication skills, and advanced English proficiency.

Build and support production-grade UiPath RPA, API, browser, document, and AI-driven automations. Own automation projects end to end, including requirements gathering, process documentation, development, testing, deployment, orchestration, and production support. Use UiPath Maestro or comparable orchestration tools for durable, fault-tolerant workflows, while managing code through Git. Automations may operate on sensitive healthcare, financial, and clinical systems.

Build and publish AI-driven computer vision systems for real-time sports video analysis, large-scale game analytics, and fan-facing features. Collaborate with CMU researchers, take prototypes to production, and mentor students while deploying league-wide analytics platforms.