Top AI Engineer Jobs

Build and operate production AI capabilities across enterprise platforms, including agent runtimes, RAG, model routing, tool calling, guardrails, evaluation, monitoring, and cost optimization. Ship reliable, safe, measurable AI features; develop evaluation frameworks and quality controls; monitor production behavior; and collaborate with client-facing product, engineering, platform, backend, and security teams to deliver practical AI solutions.

Lead hands-on architecture, development, deployment, and optimization of enterprise GenAI, RAG, Agentic AI, and Neo4j Knowledge Graph solutions. Build data pipelines, APIs, microservices, and cloud-native AI infrastructure; rapidly deliver customer POCs and production systems. Serve as the primary technical interface for enterprise clients, troubleshooting performance, scalability, reliability, and cost issues while providing technical consulting.

Designs and deploys production-grade agentic AI systems for enterprise workflows. Responsibilities include intelligent agent architectures, multi-agent orchestration using MCP and A2A, RAG pipelines with knowledge graphs, memory architectures, agent benchmarking, LLM or SLM fine-tuning, edge optimization, API integration, process automation, and leading development teams through enterprise adoption.

As Agentic AI Architect, you'll design AI agent ecosystems, lead architectural development, mentor engineers, and ensure solutions meet business challenges in various domains.

Build and scale AI-enabled go-to-market systems that acquire, route, nurture, and convert leads into qualified pipeline. The role connects Marketing, Sales, and RevOps, improves audience targeting and funnel conversion, runs channel experiments, automates workflows, and owns marketing technology, campaigns, copy, and reporting.

Build scalable evaluation, introspection, visualization, and metrics tools for autonomous vehicle machine learning models. Develop workflows processing billions of examples and connect offline evaluation with data, training, simulation, and launch decisions. Collaborate with modeling, data, infrastructure, and validation teams to improve model performance, safety, reliability, and development speed. Responsibilities include software design, implementation, maintenance, code reviews, and production tooling across backend, frontend, and data infrastructure.

Build and deploy production AI systems, including LLM agents, retrieval and API integrations, cloud-based model operations, optimization algorithms, and time-series forecasting. Apply machine learning to robot fleet routing, coordination, operational telemetry, anomaly detection, and predictive analytics. Partner cross-functionally as an AI subject matter expert while ensuring reliability, scalability, cost efficiency, and performance when models interact with physical systems and hardware.

Lead AI platform architecture, design microservices and REST APIs, mentor engineers, and drive Agile practices for AI systems at scale.

Own applied AI research from problem framing through experimentation, evaluation, prototyping, and production deployment. Build agentic systems, evaluation platforms, model-serving solutions, and self-improving workflows across hosted and self-hosted models. Measure quality, cost, reliability, and speed; advise deployment teams; mentor engineers; and improve coding-agent practices.

Designs, develops, and maintains generative AI systems and agentic AI integrations. Collaborates with stakeholders to gather requirements, tunes models and prompts, converts unstructured inputs to specs, implements secure cloud-native services, performs testing and production support, mentors engineers, and ensures observability, privacy, and continuous improvement.

Leads hands-on development of agentic AI solutions on NEO, including agent execution, sandboxing, integrations, memory, retrieval, evaluations, guardrails, and authorization. Builds full-stack TypeScript and Next.js applications, writes secure production code, improves engineering automation, and supports operational stability. Mentors engineers, promotes responsible AI-assisted development, establishes validation standards, and drives adoption of emerging technologies across Software Engineering.

Architect and develop Java-based AI, IVR, conversational, NLP, and LLM solutions for high-volume customer interactions. Build classification and clustering models, analyze data with SQL, design RAG and prompt-engineering workflows, and lead agentic AI development. Establish LLMOps practices for monitoring, evaluation, and lifecycle management while partnering with platform, speech engineering, and product teams. Mentor junior and offshore engineers.

Build and integrate enterprise AI solutions using pre-trained LLMs, RAG architectures, traditional machine learning, AI agents, and deterministic logic. Develop backend APIs, React interfaces, data pipelines, and integrations with ServiceNow, SAP, Salesforce, and Azure. Support testing, deployment, monitoring, documentation, and workflow orchestration using LangChain and n8n while collaborating with AI, data, and platform teams.

Build, fine-tune, evaluate, and deploy production AI systems spanning vision, multimodal, generative, agentic, simulation, and robotics applications. Develop data and inference pipelines, optimize GPU workloads, monitor model performance, and improve robustness and safety. Collaborate with researchers, engineers, and customers to translate business needs into scalable AI solutions using NVIDIA, cloud, and MLOps technologies.

Leads growth engineering for Hiya’s web presence, translating marketing strategy into high-converting experiences. Responsibilities include migrating Webflow to a headless CMS, architecting React and Next.js systems, optimizing performance and SEO, implementing analytics and experimentation, integrating AI and third-party APIs, and scaling localized websites across global markets. The role owns website KPIs, attribution, conversion optimization, accessibility, and collaboration across marketing, product, CRM, and PLG initiatives.

Designs, optimizes, and deploys machine learning models for resource-constrained edge devices, including mobile and embedded platforms. Responsibilities include model compression, quantization, pruning, hardware-aware optimization, performance profiling, production deployment, and systems engineering. The role also addresses on-device privacy and security and collaborates cross-functionally. Preferred experience includes custom NPU/DSP toolchains, federated learning, safety-critical deployments, open-source contributions, and on-device LLM optimization.

Own and improve the end-to-end AI engine powering real-time voice drive-thru agents. Design conversational and order-taking logic, address turn-taking and noisy audio challenges, build evaluation infrastructure, and ensure accurate recommendations, substitutions, pricing, and POS integrations across restaurant brands. The role involves researching and deploying new AI techniques, training and productionizing machine learning models, and converting ambiguous quality issues into measurable improvements.

Leads software engineering within an agile Digital Utilities team, designing, developing, testing, troubleshooting, and maintaining secure, scalable production systems. Reviews code, improves application resiliency and operational stability, automates recurring remediation, and promotes enterprise-approved AI-assisted development practices with rigorous validation for security, correctness, and performance. Evaluates architectural solutions with vendors and internal teams while supporting financial services technology objectives.

Leads development of secure, scalable data and AI platform solutions, including batch and real-time pipelines, ETL/ELT workflows, data modeling, governance, cataloging, lineage, quality frameworks, and semantic layers. Builds Agentic Autonomous Lakehouse capabilities for governed self-service provisioning and operational optimization. Partners with technical and business stakeholders, reviews production code, improves system stability, evaluates architectures and vendors, and promotes emerging technologies across engineering communities.

Leads federal data and AI engagements from planning through delivery, including client workshops, data discovery, ETL/ELT, annotation, machine learning model development, validation, deployment, and reporting. Oversees complex workstreams, budgets, risks, deliverables, dashboards, and stakeholder expectations. Develops production-level AI software using distributed computing, big data platforms, cloud infrastructure, NLP, information retrieval, and machine learning on unstructured data.

Build and deploy AI systems for supply chain optimization, including LLM-powered Text-to-SQL and RAG applications, proactive anomaly alerts, and autonomous agents. Develop machine learning models, data pipelines, and secure human-in-the-loop workflows. Collaborate with engineers, data scientists, analysts, and supply chain stakeholders to translate logistical challenges into production-ready AI solutions.

The AI Engineer will develop AI/ML solutions for investment analysis, design applications, optimize models, and work with large datasets in healthcare and finance contexts.

Build and operate enterprise-scale AI solutions, including multi-agent systems, LLM architectures, RAG pipelines, distributed training, online inference, and low-latency serving. The role uses Vertex AI and Google Cloud services to create secure, compliant, observable, and production-grade AI workflows. Responsibilities include agent orchestration, governance, monitoring, automation, stakeholder collaboration, and ownership of the complete AI development lifecycle from data collection through deployment and performance monitoring.

Own the company’s AI intelligence layer end to end, including reasoning, learning loops, evaluations, guardrails, and reliability. Build and deploy applied LLM systems for real-world users, establish the AI architecture, identify and address failure modes, and eventually hire and lead the AI team. The role emphasizes production AI judgment and systems building over model research or industry experience.

Design, build, and deploy AI/ML and generative AI solutions for internal teams and biopharma clients. Integrate AI APIs, create prototypes and production applications, develop data pipelines, and deploy scalable cloud-based systems. Build client-facing tools, dashboards, and intelligent services while evaluating emerging AI technologies. Collaborate with cross-functional teams, provide technical guidance, gather requirements, support platform rollouts, and promote responsible AI, LLM, RAG, prompt engineering, and agent best practices.