Top AI Engineer Jobs

Build and scale AI/ML-enabled features across frontend, backend, and data pipelines. Productionize ML models, deploy MLOps workflows, integrate inference APIs, optimize data models, ensure reliability, and collaborate with cross-functional teams for mission-critical analytics.

The Senior Associate will build and extend the data platform and AI applications, focusing on data engineering, cloud infrastructure, and AI development.

Design and lead data science solutions: ingest and analyze structured and unstructured data, build predictive models and NLP systems, integrate enterprise tools, and advise clients while guiding teammates on AI/ML workflows and deployments.

Design, prototype, and deliver enterprise AI solutions (RAG, LLMs, agents). Architect data flows and integrations, evaluate third-party models, build production features, establish telemetry and evaluation, partner with stakeholders, maintain AI roadmap, and evangelize AI adoption across business units.

Lead a small team to research, develop, train, optimize, and deploy computer vision and multimodal fusion AI/ML models for remote sensing (EO/SAR). Analyze large datasets, translate research into operational systems, present technical findings to customers, and produce supporting documentation and user guides.

The role involves developing AI/ML algorithms in various disciplines, analyzing datasets, and mentoring team members within a research environment.

Develop and manage the research infrastructure for AI at Databricks, focusing on large-scale training, orchestration, and improving developer productivity.

Build and maintain benchmarks and evaluation frameworks for document understanding, run experiments, produce weekly technical content (blogs, reports, tutorials), publish analyses comparing models, contribute open-source examples and notebooks, and collaborate with ML teams to highlight capabilities and surface improvements.

Design, develop, and deploy AI-enabled backend services and agentic workflows that integrate ML models, APIs, and retrieval/RAG systems. Build scalable, containerized cloud-native architectures on AWS, implement data pipelines and APIs, ensure performance, reliability, and SDLC best practices, and collaborate with researchers to operationalize machine learning in mission environments.

Design and build a production multi-agent harness and MCP integrations that operate across ERPs, CRMs, and business tools. Own end-to-end production concerns (reliability, observability, permissions, safe workflow execution), scope the product, make architecture decisions, and ship features working closely with CTO, product, engineering, and design. Stay current with agent frameworks and use tools like Claude Code in development.

The AI Engineer will develop and optimize AI models and high-performance software systems for drug discovery, collaborating with researchers and maintaining infrastructure.

Build and maintain full-stack web and mobile-friendly applications, design and manage Airtable backends, implement workflow automations (n8n/Zapier), integrate LLM/AI services (e.g., Anthropic Claude), optimize performance, collaborate with leadership and vendors, and document systems to reduce single-owner risk.

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.

Develop, test, and improve AI-powered voice and chat agent workflows: write scripts, configure conversational flows, analyze user data, perform QA, troubleshoot agent behavior, and collaborate with engineers and designers to optimize performance.

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.