Top AI Engineer Jobs

Own the end-to-end development of an AI assistant using AWS Bedrock, including agent and prompt design, tool integration, guardrails, evaluations, and access controls. Build supporting React, Node.js, GraphQL, Elasticsearch, AWS, and data-layer functionality. Use AI-native, spec-driven development with Claude Code, review generated code, and ensure quality, security, performance, and maintainability across a distributed SaaS platform.

This 12-week AI engineering internship supports the development and implementation of generative AI solutions. Responsibilities include research, analysis, AI engineering projects, data processing, report generation, operational support, and collaboration across teams. Candidates will apply knowledge of Python, LLMs, prompt engineering, vector databases, RAG, APIs, data engineering, and cloud development while pursuing a relevant degree.

Supports research, analysis, prototyping, and evaluation of AI, machine learning, blockchain, stablecoin, digital asset, and payment technologies. Develops AI proof-of-concepts, scripts, prototypes, and test automation solutions; assesses emerging technologies; documents and presents recommendations; and collaborates with engineering, product, cybersecurity, business, and executive stakeholders.

Build and operate end-to-end agentic AI products from initial concept through production and product-market fit. Responsibilities include developing agents, retrieval and orchestration systems, integrating tools, shaping product direction with design and product partners, hardening systems after launch, and creating reusable engineering primitives. The role requires entrepreneurial ownership, production AI experience, strong engineering judgment, and the ability to work effectively with technical and commercial stakeholders.

Leads the design, development, and deployment of production enterprise AI applications, intelligent automation, agentic workflows, and RAG solutions. Builds integrations with business platforms, implements observability, evaluation, security, governance, and human-in-the-loop controls, and establishes reusable AI engineering standards. Partners with business, product, data, security, and engineering teams to deliver measurable value while mentoring engineers and evaluating emerging AI technologies.

Build the world-model intelligence layer for a general-purpose humanoid robot. Responsibilities include developing predictive world simulators and model-based reinforcement learning systems; applying transformers, diffusion models, NeRFs, and recurrent state-space models; implementing research in Python and C++; integrating models with ROS2 and simulation environments; closing the sim-to-real gap; and converting research prototypes into tested, production-ready systems for real-time robotics.

Build and optimize production-grade agentic AI systems for marketing and enterprise customer experience use cases. Responsibilities include agent orchestration, tool use, RAG, embeddings, guardrails, LLM evaluation, observability, performance optimization, and scalable Python services deployed on AWS. The role partners across product and engineering teams and requires hands-on experience with LLM agents, marketing data platforms, retrieval systems, and AI evaluation.

Build and ship full-stack AI applications, including frontend experiences, backend services, retrieval systems, MCP tool layers, workflows, and intelligent agents. Own projects from concept through production while ensuring reliability and scalability. Develop AI evaluation and feedback systems, debug complex issues across the stack, and improve system performance. The role requires production experience with frontend and backend development, databases, asynchronous systems, and applied AI or machine learning.

Design, build, and deploy production-grade AI applications end to end. Develop stateful multi-agent workflows with LangGraph, rapidly prototype full-stack applications, and create automated LLM evaluation systems measuring accuracy, latency, hallucinations, and regressions. Integrate AI safely into business workflows, work with vector and relational databases, provide framework feedback, and mentor teammates while owning applications from concept through production.

Build prototypes and utility tools using GenAI and open-source LLMs, evaluate low-code and no-code automation platforms, and identify business-process automation opportunities. Translate business needs into technical requirements and user stories, support AI-enabled workflows, train internal teams, and document tools and processes. The role bridges business and technical teams while improving operational efficiency across Medscape Education.

Design, build, evaluate, and deploy production-grade AI applications end to end. Develop stateful multi-agent workflows with LangGraph, rapidly prototype full-stack MVPs, and create automated LLM evaluation systems measuring accuracy, latency, hallucinations, and regressions. Integrate AI safely into business workflows, work with vector and relational databases, provide framework feedback, and mentor teammates while balancing technical complexity with intuitive user experiences.

Develop and maintain enterprise data-intensive and AI-enabled applications supporting product development and quality teams. Build optimized SQL transformations, Databricks pipelines, Java/Python backend services, APIs, and cloud-native applications using Kubernetes and Docker. Partner with AI teams to productionize ML and LLM solutions, implement testing, CI/CD, observability, security, and reliability practices, troubleshoot production issues, shape architecture, and mentor engineers.

Build and ship driver-facing AI experiences for GM vehicles using Kotlin and Android Automotive. Own features end to end across conversational behavior, on-device fulfillment, vehicle integration, UI, connected services, validation, and iteration. Prototype and productionize intelligent capabilities, diagnose issues across application and cloud boundaries, establish engineering patterns, collaborate cross-functionally, mentor engineers, and influence the roadmap for in-vehicle AI products.

Develops AI applications supporting defense, cyber, intelligence, and mission environments. Responsibilities involve programming with Python or JavaScript/TypeScript, working with vector databases, embeddings, model APIs, evaluation frameworks, agent testing harnesses, model hosting, inference pipelines, and cloud AI platforms. The role also requires secure AI development, data protection, human-in-the-loop workflows, AI assurance, and evaluation methodologies in government settings.

Lead customer-facing design, deployment, and operation of agentic AI solutions on the ServiceNow platform. Analyze customer workflows, architect integrations and guardrails, deploy solutions to production, verify reliability and security, establish reusable technical standards, mentor engineers, advise executives, and feed field learnings back into product and engineering.

Design, build, and deploy production AI agents and orchestration systems using tools like LangChain and LlamaIndex. Apply prompting, RAG, fine-tuning, and model selection. Architect agent reasoning and workflows, implement monitoring and evaluation, and integrate AI systems with backend and frontend services for scalable, reliable, compliant production deployments.

Design and deploy agentic AI systems that automate multi-step financial workflows. Architect agents using frameworks like LangChain and LlamaIndex, apply prompting, RAG, and fine-tuning, optimize reasoning and tool use, build feedback-driven systems, and integrate AI reasoning securely into production backend and frontend services.

The Senior AI Engineer will build and scale AI features, develop AI agents, and partner with product teams to enhance products for small businesses.

As a Staff AI Engineer at Fieldguide, you will define technical direction, build AI systems, mentor engineers, and drive organizational leverage across various teams, while focusing on enterprise-grade reliability.

The role involves designing and building AI agents for audit workflows, owning product areas end-to-end, and mentoring other engineers.

As an AI Engineer at Fieldguide, you'll develop AI agents for complex audit workflows, design systems for reliability, and optimize agent capabilities based on customer needs. You'll build and integrate systems using LLMs and ensure agents improve over time, collaborating closely with product and design teams.

As an AI Engineer, you will create a unified evaluation infrastructure for AI agents, ensuring reliable performance while integrating automated pipelines and feedback systems for rapid model evaluation and quality assurance.

Lead design, deployment, and production scaling of LLM-based and agentic AI systems; evaluate and benchmark models; build prototypes and production AI pipelines; collaborate with engineering and business stakeholders; influence leadership and mentor junior team members.

Design, build, and scale production LLM, retrieval, and agentic AI systems for compliance automation. Implement evidence-grounded, explainable workflows, optimize for latency/cost/reliability, and embed human-in-the-loop guardrails while collaborating cross-functionally.

Build and deploy production-scale AI/LLM systems: own ML lifecycle from model development to serving, optimize GPU/Kubernetes deployments, design LLM applications and APIs with monitoring, apply CI/CD and version tracking, collaborate with product teams, and contribute to AI engineering best practices.