Top AI Engineer Jobs

Design, implement, and optimize embedded firmware and NPU/DSP runtimes for on-device ML. Build realtime sensor-to-inference pipelines, optimize latency, memory, and power, debug hardware/firmware interactions, and collaborate with ML, electrical, and product teams to deliver production-grade edge intelligence.

Build and ship full-stack features for a production AI/LLM platform: implement polished React UIs from specs, own database and access-control/security, integrate multi-provider LLMs (RAG, agentic workflows), and formalize QA by extending test suites with integration and end-to-end coverage.

Design, build, and scale an enterprise AI/ML platform and edge inference systems. Develop FastAPI Python services, CI/CD pipelines, ML orchestration (LangGraph, MLflow), data and knowledge pipelines (RAG, knowledge graphs), deploy models to edge GPUs (NVIDIA Jetson) via Azure IoT Edge, and own reliability, observability, and cost optimization. Hybrid role in Atlanta.

Design, build, and operate end-to-end data and AI systems: scalable ETL pipelines, model development and deployment, backend services/APIs, cloud infrastructure, MLOps, data governance, and lead technical reviews while mentoring engineers and collaborating with stakeholders.

Design, build, test, and deploy AI agents, agentic workflows, and automation solutions. Partner with stakeholders to prototype and productionize full-stack AI applications across cloud, VPS, and local GPU environments while ensuring scalability, security, and performance.

The Internal Systems Engineer builds AI-powered tools and maintains a shared agent platform while integrating AI into customer operations at impact.com.

Build and ship end-to-end product features, debug and resolve production issues, partner with Product on UX and direction, use AI heavily for development and prototyping, and iterate quickly across the stack to improve quality and speed.

Lead technical delivery of AI-enabled web apps, dashboards, and automation. Build full-stack systems (frontend, backend, infra), integrate LLMs, RAG, vector DBs and third-party services, prototype then productionize solutions, work client-facing for scoping and translation, and provide technical leadership across architecture, standards, and documentation.

Design and build the intelligence layer: retrieval and memory systems, RAG pipelines, feedback and evaluation loops, RL and fine-tuning experiments, LLM integration, observability for AI performance, and collaborate to productize AI features.

Lead production agentic development and build R&D tooling that accelerates team workflows. Ship weekly in the main codebase, create and improve custom agents, MCP integrations, evals, and codegen pipelines, train engineers on new workflows, and raise team standards for prompts, tooling, and autonomous throughput.

As an AI Prompt Engineer, you'll develop prompt templates, conduct A/B tests, collaborate with trainers, and maintain prompt libraries for generative AI applications.

Build and improve AI/ML systems and data pipelines for a B2B SaaS platform; shape product and technical strategy; work with customers; mentor team members; ensure secure, robust, and scalable infrastructure.