Top AI Engineer Jobs

Build production-quality AI applications for federal clients and internal products. Responsibilities include gathering requirements, designing prompts and retrieval workflows, integrating APIs and data, creating evaluations, investigating failures, documenting limitations, and supporting proofs of concept and client demonstrations. The role requires client-facing communication, comfort with ambiguity, and the ability to translate business needs into working software.

Design, develop, deploy, maintain, and optimize scalable AI applications, especially LLM-based solutions. Responsibilities include supporting existing applications, researching datasets, applying statistical and machine learning techniques, evaluating and fine-tuning models, testing with end users, and tracking advances in AI. The role requires production-quality Python development, front-end experience, collaboration, autonomy, and the ability to translate technical concepts for varied audiences.

Design, build, deploy, and support production-grade integrations between the Blue Machines platform and client systems. Develop API, cloud, and AI/LLM integrations; collaborate with clients on deployment, debugging, and optimization; and automate, document, and monitor workflows. The role requires Python or Java backend experience, cloud expertise, web technologies, authentication protocols, AI/LLM familiarity, and strong problem-solving and communication skills.

Build and own production AI features for permitting workflows, including document extraction, retrieval, agentic systems, evaluation, observability, deployment, and iteration. Define quality standards, improve LLM reliability, make product and architecture decisions, mentor engineers, and work directly with users. The role requires substantial production LLM experience and in-person work four days weekly in the San Francisco Bay Area.

Build and own Android growth and monetization experiments across subscriptions, pricing, advertising, purchases, and upgrade flows. Partner with product, design, data science, and analytics to instrument experiments, manage staged rollouts, monitor performance, and act on results. Use AI tools throughout development, contribute to Android architecture and engineering practices, participate in on-call support, mentor engineers, and help scale reliable mobile experiences to tens of millions of users.

Develop and evaluate multimodal deep learning and reasoning models for Edge AI, covering data collection, annotation, preprocessing, training, post-training, reinforcement learning, knowledge distillation, and rigorous evaluation. Build agentic and multi-agent reasoning systems, modify transformer architectures, and prototype in Python using PyTorch or TensorFlow. Document findings through reports, publications, and conference submissions while exploring emerging approaches such as world models, JEPA, diffusion, and deep research automation.

Develops multimodal deep learning and reasoning models for Edge AI, including time-series, audio, video, and sensor data. Responsibilities span data collection, annotation, preprocessing, model training, evaluation, supervised fine-tuning, reinforcement learning, knowledge distillation, transformer modification, and multi-agent orchestration. The role also prototypes emerging AI methods, documents research, and publishes results. Requires extensive AI/ML product experience, a related PhD, and a strong publication record.

Lead technical discovery for complex engineering simulation use cases, deliver customer demonstrations, workshops, and proofs of concept, and support technical sales engagements. Partner with account executives and AI/ML, product, and engineering teams to identify risks, integration needs, product gaps, and new customer use cases. Develop reusable demos and solution patterns while helping customers apply Physics AI, simulation, and automation to engineering workflows.

Lead end-to-end development and deployment of enterprise generative AI and LLM applications, including agentic workflows, MCP-based data integrations, secure tool usage, AI gateways, identity propagation, access controls, and observability. The role partners with Information Security and corporate functions to deliver governed AI automation across procurement, supply chain, legal, finance, HR, and manufacturing.

The posting provides limited role-specific details. Based on the title, this position focuses on DevOps engineering enhanced by AI tools and practices within a healthcare technology company. The role likely supports cloud infrastructure, deployment automation, reliability, and development operations, but specific responsibilities, technologies, qualifications, compensation, and work arrangement are not provided.

Build, prototype, test, and scale AI-enabled software solutions from concept through enterprise production. Develop applications in Microsoft Azure and other cloud environments, collaborate with business and clinical users, and contribute to architecture, data pipelines, security, testing, documentation, and DevOps practices. The role also helps establish team standards, evaluates emerging AI development tools, and ensures compliance with data governance, privacy, and protected health information requirements.

The Machine Learning Engineer will design and implement ML pipelines and productionize models, collaborating with scientists and engineers on scalable systems.

Lead the architecture, deployment, and scaling of agentic AI infrastructure for embedded memory design, characterization, DFT, silicon bring-up, and yield optimization. Develop multi-agent LLM workflows, integrate AI systems with EDA tools and enterprise data, automate scan-failure analysis, build predictive yield models, and establish evaluation, testing, and safety guardrails. Collaborate with memory designers, DFT experts, EDA specialists, and infrastructure engineers to deliver production-grade AI engineering solutions.

Develop and deliver secure, AI-enabled, cloud-native full-stack solutions for lending platforms. Build APIs, integrations, reporting capabilities, and applications using Azure, .NET, Python, React, SQL, and ETL technologies. Apply Prompt Engineering, MCP, and RAG patterns; implement CI/CD pipelines; support observability, governance, security, and compliance. Collaborate with architects, business partners, QA, and engineers, participate in reviews, guide junior developers, and maintain technical documentation.

Build and maintain AI applications and agents from prototype through deployment. Translate client business problems into technical solutions, connect applications to Power BI, Microsoft Fabric, and business systems, troubleshoot reliability and performance issues, and validate AI responses. Lead client discussions, demonstrations, training, milestone tracking, and scope management while creating reusable templates and documentation. Use agentic coding tools, evaluate emerging AI technologies, and collaborate with consultants and clients.

Build and lead a scalable platform engineering function spanning CI/CD, infrastructure, observability, developer tooling, environments, and production operations. Develop internal tools, reusable automation, paved roads, and AI-assisted workflows that improve developer experience, delivery reliability, and engineering productivity. Partner across teams, guide architecture and standards, measure DORA and DX metrics, and shape the platform roadmap while providing hands-on technical leadership.

Designs, builds, tests, and deploys enterprise AI solutions using low-code and pro-code tools. Leads business-requested AI engagements, develops agents, workflows, integrations, MCP components, and knowledge-retrieval solutions. Partners with stakeholders to translate requirements into secure, governed, measurable solutions while managing request queues, documentation, support transitions, and reusable engineering patterns. Applies enterprise identity, authentication, authorization, data protection, and responsible AI practices.

Build and deploy production AI, machine learning, and computer vision systems for aircraft manufacturing. Responsibilities include developing AI-enabled services, APIs, data pipelines, visual inspection and anomaly detection solutions, model safeguards, monitoring, and integrations with factory systems and equipment. The role owns end-to-end delivery, leads technical design, mentors engineers, and establishes scalable practices for reliable AI deployment.

Build foundational MLOps and AI infrastructure for petabyte-scale multimodal media datasets. Responsibilities include data ingestion, storage, indexing, retrieval, model fine-tuning and inference infrastructure, monitoring, validation pipelines, cloud cost optimization, and CI/CD. The role also involves distributed systems, scalable APIs, automation, and hybrid cloud or media-processing infrastructure.

Build and operate secure, scalable platform infrastructure for AI-driven applications across cloud, on-premises, and edge environments. Own SRE practices, CI/CD, deployment automation, observability, performance optimization, incident response, and reliability improvements. Develop Python-based internal tools, partner with software teams on production readiness, and support customer-site installations and lifecycle operations. The role also emphasizes security, compliance, developer productivity, and distributed-system troubleshooting.

Build and ship marketing sites, landing pages, and microsites across five product brands using AI coding agents. Maintain distinct brand identities, reusable design systems, SEO foundations, information architecture, and frontend quality. Review and correct AI-generated code, copy, and design; conduct competitive research; manage cross-brand component decisions; and use browser inspection and screenshot-based critique loops to deliver production-ready work.

Build and deploy AI-enabled internal applications, dashboards, workflow automations, reporting tools, and system integrations for insurance operations. Develop web solutions, APIs, and database connections; integrate insurance, cloud, and SaaS platforms; and test systems supporting underwriting, billing, claims, reporting, and security. Translate operational needs into technical solutions while collaborating with internal teams, vendors, and nontechnical stakeholders. The role requires full-stack or backend development experience and production experience with AI/ML or LLM applications.

Build production AI systems that improve technical learning for Hivemind engineers and customers. Develop an AI training coach, LLM-powered tooling, workshops, curricula, labs, assessments, and content automation. Integrate generative AI, retrieval, agents, and evaluation capabilities while maintaining strong software engineering, testing, observability, security, and operational standards. Collaborate with autonomy experts, engineers, applications engineering, and customer success to accelerate technical proficiency and product adoption.

Partner with business stakeholders and IT delivery teams to design, implement, and deploy AI-enabled production applications, integrations, and data pipelines. Focus on solution feasibility, deployment, operationalization, and driving adoption. Support benchmarking and internal development across divisions (travel up to 50%).

Lead design, training, and deployment of large-scale ML models (LLMs, diffusion, GNNs). Own end-to-end training pipelines, distributed training, and evaluation. Collaborate with data and infra teams, drive architecture and inference decisions, and provide technical leadership and mentorship.