Top AI Engineer Jobs

Design, implement, test, and ship secure, high-performance C++ features for Microsoft Edge, focusing on enterprise security, Data Loss Prevention, and Copilot integrations. Lead architecture across browser, cloud, identity, compliance, and management systems; collaborate cross-functionally to deliver scalable customer solutions. Mentor engineers, influence technical strategy, and improve product quality through telemetry and customer feedback.

Design, build, and operate globally distributed AI infrastructure that routes requests, prioritizes traffic, and allocates GPU capacity. Develop reliable, secure, and scalable services; collaborate with product and partner teams to launch AI capabilities; support production systems through monitoring, on-call rotations, troubleshooting, and gradual rollouts. Mentor engineers, conduct code reviews, and apply AI tools throughout the software development lifecycle.

Build integrations, automation, analytics pipelines, dashboards, and self-service tools for enterprise workforce AI platforms including Microsoft Copilot, Claude, and Glean. Develop adoption and usage reporting, automate platform administration and licensing governance, support AI enablement programs, and integrate privacy, security, and responsible AI practices. Lead complex projects, mentor developers, collaborate with stakeholders, demonstrate solutions, and recommend technology and process improvements.

Build and lead development of Ark, an AI-native software development and secure delivery product. Responsibilities include creating AI agent workflows, isolated execution environments, Kubernetes and UDS integrations, persistent asynchronous systems, automated testing and validation, and cloud-native deployment tooling. The role involves architecture decisions, complex debugging, reliability and security improvements, code reviews, mentoring, and collaboration with Product, UX, and mission-focused customers.

Operate and improve enterprise AI/ML platforms across Palantir Foundry, AWS Bedrock, SageMaker, and cloud-native services. Responsibilities include MLOps automation, deployment, monitoring, observability, governance, incident response, root cause analysis, reliability engineering, and lifecycle management for production AI workloads. The role also provides technical leadership, mentorship, and cross-functional collaboration while supporting Generative AI, LLM, and RAG solutions.

Leads systems software prototyping and workload characterization for Azure hardware/software co-design. The role optimizes AI inference and LLM workloads, especially KV Cache placement, migration, and utilization across GPUs, CPUs, CXL memory, storage, and networks. Responsibilities include developing inference framework extensions, instrumentation, performance models, simulation frameworks, and memory-tiering proof-of-concepts while influencing long-term architecture and collaborating with hardware, software, workload, and industry partners.

Build and deploy enterprise agentic AI solutions, including agents, MCP tools, plugins, orchestration architectures, evaluation harnesses, and secure APIs. Integrate agents with search and collaboration platforms, instrument production behavior, manage cost and latency, and test against prompt injection and tool vulnerabilities. The role requires backend software engineering, production agent development, MCP experience, LLM evaluation, and AI coding assistant expertise, with preferred experience in managed runtimes, retrieval systems, and enterprise security.

Lead development and deployment of an agentic AI/ML pipeline for identifying, prioritizing, and pricing high-cost healthcare claims. Responsibilities include model development, confidence scoring, HITL safety design, ML lifecycle management, monitoring, workflow automation, responsible AI, and enterprise-scale analytics using Databricks and Snowflake. The role also evaluates emerging AI technologies and supports regulatory-aware automation and healthcare data transformation.

Designs, deploys, and scales AI and machine learning solutions for enterprise healthcare applications. Responsibilities include building and tuning production models, developing MLOps frameworks and deployment pipelines, monitoring model performance, automating workflows, evaluating emerging AI technologies, and ensuring security, scalability, responsible AI, and governance standards. The role collaborates with software engineers, data scientists, and business stakeholders.

Architect and build production-grade AI platforms and autonomous security agents for vulnerability discovery, validation, and remediation. The role focuses on agentic orchestration, LLM and generative AI integration, AI red-teaming, model deployment, MLOps, observability, evaluation, safety, and scalable APIs. The engineer will partner across Product, Security Research, and Engineering while advancing responsible AI capabilities.

Leads the design and implementation of security solutions for AI systems across data ingestion, model training, deployment, and runtime. Develops controls against prompt injection, data poisoning, model inversion, data leakage, and unauthorized access. Secures cloud infrastructure, APIs, identities, integrations, and third-party AI platforms; implements monitoring, automated security testing, and incident response. Partners with architecture, engineering, and governance teams to ensure resilient, compliant AI security practices.

Lead security-focused DevSecOps efforts for Cisco AI products by embedding security throughout the SDLC, integrating security testing into CI pipelines, managing vulnerabilities and compliance, conducting architecture reviews, threat modeling, red teaming, and maintaining organizational risk registers. Secure cloud infrastructure, runtime workloads, and Infrastructure as Code across major cloud platforms while improving developer remediation workflows.

Lead security engineering for Cisco AI products by embedding shift-left security into the SDLC, integrating security testing into CI pipelines, managing vulnerabilities and compliance, conducting architecture reviews, threat modeling, red teaming, and maintaining risk registers. Secure cloud workloads and infrastructure across AWS, GCP, and Azure using cloud security platforms and Infrastructure as Code controls.

Design, build, and operate end-to-end AI-enabled revenue cycle management features. Implement integrations, improve reliability, optimize performance, and ship production LLM-powered agents with monitoring, observability, and architectural ownership.

Drive adoption of AI-driven security solutions through customer-facing technical engagements. Lead AI risk and governance workshops, support proof-of-value and proof-of-concept projects, demonstrate products, and advise on integrating AI security with existing architectures. Collaborate with Sales, Product Management, Marketing, and Customer Success while translating complex cybersecurity and AI concepts into business value.

Owns the end-to-end development of iOS monetization experiences, including paywalls, pricing, subscriptions, upgrades, and purchases across global storefronts. Partners with product, design, analytics, finance, and legal to prototype, experiment, launch, and optimize reliable consumer-scale features. Responsibilities include architecture, performance optimization, staged rollouts, code reviews, mentoring, and advancing AI-native engineering practices.

Build and deploy production-grade agentic AI systems that automate enterprise workflows and support decision-making. Responsibilities include developing autonomous and semi-autonomous agents, benchmarking model performance, designing scalable AI/ML systems, conducting experiments, and improving evaluation and monitoring. The role requires collaboration with engineering and product partners, alignment with security and compliance standards, and application of software engineering, cloud, DevOps, machine learning, and MLOps practices.

Design and implement scalable distributed systems for Azure AI traffic management, scheduling, routing, capacity allocation, and workload optimization. Build control-plane services, observability tooling, and automation for reliable, low-latency LLM inference across global infrastructure. Investigate production issues, improve resiliency and fleet efficiency, onboard models and regions, mentor engineers, lead cross-team initiatives, and influence long-term AI infrastructure strategy.

Lead development of AWS-based security platforms that orchestrate SAST, SCA, container, and API security scanning across enterprise pipelines. Build AI-enabled application security capabilities, including agentic vulnerability analysis, remediation recommendations, validation, and quality measurement. Design APIs, event-driven services, automation, and CI/CD integrations while improving platform reliability, observability, and scalability. Provide technical leadership, mentorship, and strategic direction across DevSecOps, application security, cloud, and AI initiatives.

Own production reliability for a Kubernetes-native GPU inference platform. Responsibilities include operating provisioning and host-lifecycle state machines, troubleshooting GPU, CUDA, InfiniBand, and RDMA failures, extending automated remediation, managing SLOs and error budgets, leading incident response, improving observability and inference efficiency, and partnering with ML platform and product teams to build reliable self-healing infrastructure.

Owns the design, deployment, governance, and operation of enterprise AI platforms and employee-facing agents. Builds secure control layers including identity, access, device, tenant, MCP, secrets, logging, rate limiting, and cost controls. Establishes AI lifecycle processes, evaluates and rolls out new tools, operates infrastructure to SRE standards, and develops telemetry, dashboards, runbooks, and incident response. Partners with Security, Legal, Privacy, and IT teams to translate governance requirements into enforceable technical controls.

Develop strategies, cost models, requirements, documentation, and operational guidance for enterprise AI token usage. Analyze LLM architectures, tokenization, pricing, context windows, embeddings, APIs, and AI platforms. Support budgeting, procurement, licensing, and infrastructure decisions; evaluate vendors and technical proposals; and brief senior government, acquisition, and technical stakeholders. The role requires extensive AI expertise, government and defense mission experience, strong technical writing, and an active TS/SCI with polygraph clearance.

Build and own AI products for sales, marketing, customer service, finance, and other business teams. Embed with stakeholders to identify workflow opportunities, define KPI baselines, design AI tools and agents, and ship prototypes through production. Maintain reliability, adoption, monitoring, and ongoing iteration while creating reusable AI patterns and enabling teams to extend the tools. The role combines product discovery and engineering, requiring strong LLM experience, product judgment, communication, and autonomous execution.

Develop and support AI-powered applications, agents, and RAG solutions using Python, AWS services, Snowflake, and modern data technologies. Design integrations, modernize legacy reporting and manual processes, and deliver scalable enterprise applications. Collaborate with engineers, data teams, and business stakeholders while participating in testing, debugging, code reviews, deployment, troubleshooting, and Agile development.

Designs and delivers production-grade agentic AI systems using LLMs, RAG, orchestration, tool use, multimodal processing, evaluation frameworks, and human oversight. The role includes building data pipelines, optimizing AI workflows for cost and reliability, deploying securely on cloud platforms, and applying containers, CI/CD, asynchronous patterns, and responsible AI practices. It also involves client collaboration, technical presentations, workshops, and solution prototyping.