Top AI Engineer Jobs

Design and implement server-side logic and RESTful APIs, manage MongoDB and Redis databases, ensure security and performance, contribute to system design and architecture, review code and scope deliverables, collaborate with Product and Client Success, and maintain documentation.

Design, build, and productionize Applied AI foundations: model access, retrieval/memory, observability, secure data paths, human-in-the-loop workflows, and reusable AI-driven platform capabilities that support product and operational teams.

Embed with core operational teams to map workflows and remove manual work by building production AI agents, copilots, and internal tooling. Deploy retrieval, memory, and evaluation systems to improve reliability, automate underwriting and claims workflows, extract structure from messy healthcare documents, and measure operational leverage through faster workflows and reduced manual processes.

Lead backend architecture and system design for an AI-driven manufacturing planning platform. Build and scale a Node.js/Python stack with Azure Databricks, implement optimization and scheduling algorithms, establish engineering patterns, and mentor/lead a small early-stage engineering team while collaborating with product and data functions.

As a Founding Full-Stack Engineer, you'll build AI-native systems, integrating LLMs, prototyping quickly, and collaborating to influence engineering standards and culture.

Design and build full‑stack data capture workflows (Next.js web, Python desktop), architect high‑throughput data pipelines with low latency, collaborate on product strategy, ship features rapidly, and establish engineering best practices for team growth.

Build AI-powered workflows, agents, and integrations to automate marketing research, creative asset preparation, campaign analysis, reporting, and internal coordination. Prototype and productionize LLM-based tools, connect data sources and ad platforms, validate automation quality with real use cases, and document systems for reuse and scaling.

Lead architecture and implementation of production agentic AI systems: build agents that plan, retrieve context, call tools, and execute workflows; design architectures for memory, tool routing, state, safety, and human approvals; create evals and observability for multi-step tasks; integrate with APIs and enterprise systems; improve reliability and guardrails; choose models and infrastructure; mentor senior engineers and own technical direction.

Lead developer-focused growth for DeepL's headless surfaces (MCP, CLI). Drive discoverability, instrument activation funnel to paid usage, create production-quality developer artifacts and content, build ecosystem presence, and feed actionable developer insights into product and engineering.

Design, test, and maintain reusable prompt templates for enterprise LLM use cases. Run A/B tests, collaborate with trainers and integrators to embed prompts into RAG and AI workflows, and keep prompt libraries updated with best practices and client guidelines.

Lead client discovery, design and deliver AI prototypes and integrations using LLMs, RAG, and agentic frameworks. Build, evaluate, and optimize models and retrieval pipelines, integrate AI into workflows, mentor teams on responsible AI, and produce evaluation and governance artifacts for federal clients.

As a Software Engineer in AI Infrastructure, you will design and develop core platform components, build APIs and services, enhance performance, and automate tooling while collaborating across teams and improving system reliability.

Architect and ship production AI systems and agent workflows with strong focus on explainability, auditability, and compliance for financial services. Build Python/FastAPI tooling, distributed event-driven backends, evaluation and monitoring pipelines, and occasional React/TypeScript UIs. Drive schema, API, and system architecture decisions and establish validation and quality-assurance patterns for AI outputs.

Design and build scalable backend services and data ingestion pipelines to process and enrich data (including LLM enrichment). Perform data modeling, mapping, and large-scale file manipulation while collaborating in an Agile team to deliver reusable APIs, optimize performance, and improve processes.

Own product quality for integrations, APIs, dashboards, and AI-agent workflows: build test plans, perform exploratory and end-to-end testing, maintain regression suites, automate high-value tests, investigate production issues, and drive defects to resolution working closely with engineers and product managers.

Lead technical architecture and direction for complex AI/ML systems, mentor engineers, and drive innovation in applied AI at GEICO.

The Senior Staff Engineer at GEICO will lead engineering teams to develop AI-powered solutions, ensuring high-quality systems and driving innovation in a fast-paced environment.

The AI Workflow Engineer will design and deliver training, facilitate workshops, redesign workflows, and develop use cases for enterprise clients using AI tools.

The Forward Deployed Engineer will embed within customer teams to solve complex business problems, drive AI adoption, and provide feedback to the Product team.

As an AI Research Engineer, you'll conduct research in AI and automated reasoning, creating innovative solutions, testing systems, and contributing to scientific publications while collaborating with engineering teams.

Lead technical discovery, solution design, demos, proofs-of-concept, and implementations for Nitra's AI platform. Partner with Sales and Customer Success to win deals and drive adoption, while feeding customer insights into Product and Engineering. Build scalable implementation processes, documentation, and resolve complex technical issues to ensure successful customer launches and expansion.

Lead technical sales and implementations for Nitra's AI platform: drive technical discovery, design, demos, POCs, integrations, and go-live. Ensure customer adoption, collect feedback to influence product roadmap, build scalable implementation playbooks, and troubleshoot complex issues alongside Engineering.

Lead design and delivery of AI-enabled engineering capabilities across the SDLC to improve developer productivity, code and test generation, CI/CD integration, and secure, governed automation. Define portfolio-level technical strategy, implement reusable automation patterns, and partner with architects, security, and platform teams to scale AI-assisted workflows from pilot to enterprise adoption.

Build and operate integrations and automations using iPaaS and APIs, ship production AI/LLM agents and workflows, implement identity-driven provisioning (SSO/SCIM/OAuth), and deliver monitored, reliable automations with runbooks while partnering across teams to scale systems.

Design and optimize high-performance kernels and custom operators for attention, MoE, GEMM, quantization and collective communication across NVIDIA, AMD and AWS Trainium. Improve LLM inference runtimes, develop distributed training/inference solutions at scale, use compilers and SDKs (Neuron, Torch Dynamo, PyTorch/XLA), contribute to open-source, and publish technical findings.