Top AI Engineer Jobs

Build and prototype agent-based AI applications and workflows, connecting LLMs, prompts, tools, and APIs. Rapidly prototype then harden code, execute CI/CD and testing, write docs/runbooks, participate in reviews, and apply secure-by-design and responsible AI practices.

Design, build, and deploy production-grade agentic applications and multi-agent workflows that automate IT and business processes. Convert prototypes into hardened services, integrate LLMs and enterprise data sources, implement CI/CD and observability, author runbooks and docs, lead reviews and threat modeling, and apply secure-by-design and responsible AI practices.

Design, build, and maintain TypeScript AWS Lambda REST APIs and cloud infrastructure for conversational AI voice agents. Implement and optimize AI agents, prompt engineering, telephony integrations (Twilio), storage, and monitoring. Follow TDD, write unit/integration tests, use AWS CDK, and collaborate with stakeholders on scalable AI solutions.

Design, develop, and deploy generative AI, LLM-powered, and agentic systems for federal clients. Lead engineering best practices, implement secure integrations, enable cloud/DevSecOps pipelines, monitor production performance, and mentor engineers while collaborating across data, cloud, and stakeholder teams.

Design and implement agentic systems using LLMs and agent frameworks, build multi-agent workflows and RAG pipelines, optimize scalable agent architectures, collaborate on experiments and feature integration, and ensure quality through reviews, testing, and documentation.

As an AI Software Engineer, you will develop Generative AI solutions, manage full-cycle development, implement MLOps pipelines, and ensure compliance with AI standards.

Design, build, and operate enterprise AI/ML platform capabilities (AIOps/MLOps/LLMOps). Develop GenAI features, experiment with models and services, and architect end-to-end solutions. Collaborate with data, cloud, and business teams, drive engineering execution, promote best practices, and mentor engineers. Ensure platform reliability, monitoring, and scalable deployments across cloud environments.

Design, simulate, and characterize RF/analog/mixed-signal ICs for space and ground; derive RFIC specifications; perform layout, packaging and parasitic modeling; validate prototypes and radio performance; develop test equipment and support production testing and qualification.

Drive end-to-end LLM inference performance on Intel GPUs: profile bottlenecks, write and optimize custom GPU kernels (attention, MoE, quantization, fusions), upstream improvements to vLLM/SGLang/PyTorch, and collaborate with architecture and compiler teams to shape hardware roadmaps.

Design, build, and maintain production-grade agent harnesses and platform capabilities for generative AI applications. Lead architecture and delivery, improve CI/CD, testing, observability, and security, mentor engineers, and collaborate with stakeholders to enable scalable, enterprise-grade agent runtimes and integrations.

Lead design and deployment of production-grade agentic AI systems for healthcare. Architect scalable multi-agent orchestration, data curation pipelines, evaluation frameworks, and translate ML research to secure, low-latency clinical applications while mentoring engineers and collaborating with clinical and product teams.

Design and maintain backend infrastructure and tooling for an AI evaluations platform. Build production-grade evaluation frameworks for ASR, LLMs, TTS, knowledge bases, and guardrails. Implement API/data integrations, test configuration management, reporting, observability, and debugging tools. Partner with ML, engineering, and QA to operationalize evaluations, enable regression detection and continuous monitoring, and improve developer experience and reliability.

Support development of LLM agents, RAG pipelines, and fine‑tuned models. Contribute to portfolio projects, run experiments and ablations, prepare technical documentation and demos, assist discovery workshops, and help implement evaluation and governance pipelines.

Build and ship agentic AI features across the full stack: design LLM-driven workflows, implement backend APIs and frontend UI, integrate MCP server capabilities, monitor production behavior, and iterate based on customer use. Drive projects independently, collaborate with product/design/engineering, and use agentic coding tools to prototype and deliver end-to-end features.

Lead development and optimization of a data‑intensive AI benchmarking stack: build Python-based backend systems and APIs, implement model evaluation and analyses, create interactive data visualizations, run custom enterprise analyses, and help evolve benchmarking methodology.

Design, build, and operate CI/CD, Kubernetes-based platforms, deployment automation, and observability for engineering workflows. Improve reliability, performance, and scalability across cloud and on-prem environments, debug cross-boundary failures, perform root-cause analysis, and deliver durable platform software and self-service tooling.

Execute hardware bring-up, validation, and telemetry monitoring for Cerebras AI clusters in data centers. Perform power-on sequencing, first-line troubleshooting, log collection, incident support under senior guidance, and contribute feedback to tooling and documentation while learning system architecture and networking fundamentals.

Design, implement, and maintain Python frameworks and services that orchestrate distributed engineering workflows across machines and clusters. Build scheduling, execution, resource management, failure recovery, and test infrastructure. Define APIs and abstractions, reason about concurrency and distributed-systems behavior, debug complex multi-system issues, write automated tests and documentation, and partner with platform, CI, release, QA, and product teams to deliver scalable infrastructure.

Integrate, validate, and productionize cross-stack inference features across AI frameworks, runtime, compiler, kernels, distributed systems, and hardware. Drive zero-to-one projects, debug system-wide failures, manage accelerated timelines, and improve automation, diagnostics, and repeatable integration practices while collaborating across software and hardware teams.

Lead design and execution of automated tests for large-scale AI clusters. Define test strategies, automate reliability, performance, stress, failure, and security tests across software (Kubernetes, monitoring) and hardware (wafer-scale accelerators, CPUs, interconnects). Debug distributed systems and datacenter hardware, and champion cluster uptime and observability.

Lead synthesis, placement, routing, and full-chip physical design for high-speed AI silicon. Perform block and full-chip physical verification, timing closure, power/performance/area optimization, DRC/LVS and IR/EM resolution, STA and constraint drafting, floorplanning, and script-based flow enhancements. Collaborate with RTL teams to integrate blocks into the full-chip architecture.

Architect and drive a unified, self-service inference control plane and reliability practices for large-scale multi-datacenter and cloud inference fleets. Build capacity orchestration, rollout safety, observability, SLO-based reliability, incident response, and automation; mentor senior SREs and measure impact through reduced toil, faster deployments, and SLO compliance.

Design, build, and maintain developer tooling for the MAX AI inference stack, including profilers, tracers, debuggers, dashboards, and agent-native tools. Integrate tooling across MAX, Mojo, and driver APIs, collaborate with kernel/model/serving engineers, and drive performance analysis, debugging, and observability to improve developer productivity and production reliability.

Design, build, and maintain infrastructure and processes to govern and scale enterprise AI tooling. Integrate data feeds for usage and cost metrics, support LLM lifecycle management and benchmarking, implement AI governance guardrails, collaborate with architecture and cross-functional teams, and create enablement documentation.

Lead design and build of AI-driven enterprise contact center and CX solutions in pre-sales: translate workflows into prototype architectures, architect retrieval/orchestration/validation systems, run technical discovery, partner with account teams, and deliver production-grade proofs of concept that handle real-world constraints and KPIs.