Top AI Engineer Jobs

Design, develop, deploy, and support foundational AI systems, including LLM training and inference, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. Optimize large-scale AI systems for cost, latency, throughput, reliability, and hardware utilization. Own complex system architecture, define AI service-level objectives, lead technical reviews, ensure security and compliance, contribute to long-term roadmaps, and mentor engineering staff.

Design, develop, deploy, and support foundational AI systems, including foundation-model training, LLM inference, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. Optimize distributed AI systems for cost, latency, throughput, scalability, and hardware utilization. Own complex architectures, define reliability objectives, lead technical reviews, ensure security and compliance, contribute to long-term AI roadmaps, and mentor engineering teams.

Designs, develops, deploys, and supports scalable AI systems, including foundation model training, LLM inference, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. The role optimizes model performance, GPU/TPU utilization, latency, throughput, and cost; owns complex AI architecture; defines reliability objectives; leads technical reviews; and mentors engineering teams.

Design, develop, deploy, and optimize production AI systems, including foundation models, LLM inference, agentic workflows, vector search, guardrails, evaluation, governance, and observability. Lead architecture and cost-performance governance for scalable AI platforms, establish engineering standards, contribute to technical roadmaps, and mentor senior AI engineers. Collaborate with scientists, engineers, technical program managers, and product managers to deliver responsible AI products.

Build and optimize foundational AI systems, including LLM training and inference, agentic workflows, vector search, guardrails, evaluation, and observability. Partner with engineering, research, product, and program teams to deliver scalable AI products. Lead model and infrastructure optimization for cost, latency, throughput, and hardware utilization; establish governance and engineering standards; contribute to technical roadmaps; and mentor senior AI leaders.

Develop and deploy foundational AI systems, including foundation model training, LLM inference, agent workflows, similarity search, guardrails, evaluation, governance, and observability. Optimize AI systems for scalability, cost, latency, throughput, and hardware utilization. Build training and deployment pipelines, improve datasets and evaluation methods, support responsible AI practices, and mentor junior engineers while contributing to long-term technical strategy.

Design, develop, deploy, and support scalable AI systems, including foundation model training, LLM inference, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. Optimize model performance, cost, latency, throughput, and hardware utilization across production systems. Own complex AI architecture, reliability objectives, and technical roadmaps while ensuring security, compliance, and ethical alignment. Lead technical reviews, collaborate across engineering and product teams, and mentor senior and principal associates.

Design, develop, deploy, optimize, and support foundational AI systems, including foundation model training, LLM inference, agentic workflows, vector search, guardrails, evaluation, and observability. Lead architecture, cost-performance governance, design reviews, and technical roadmaps for scalable production AI. Optimize model performance, hardware utilization, latency, throughput, and cost while ensuring responsible AI standards. Mentor senior technical staff and collaborate with engineers, researchers, program managers, and product managers.

Design, develop, deploy, optimize, and support production AI systems, including foundation model training, LLM inference, agentic workflows, vector search, guardrails, evaluation, governance, and observability. Lead architecture and cost-performance optimization across scalable AI platforms, establish engineering standards, contribute to long-term technical roadmaps, and mentor senior AI engineers.

Design, develop, deploy, and optimize large-scale AI systems, including foundation models, LLM inference, agentic workflows, vector search, guardrails, and multi-model orchestration. Lead cost-performance governance, technical design reviews, AI engineering standards, and long-term platform strategy. Apply state-of-the-art optimization techniques to improve model scalability, latency, throughput, hardware utilization, and cost. Mentor senior technical leaders and collaborate with engineers, scientists, product managers, and program managers.

Design and build a production multi-agent LLM framework and LLM-powered pipelines (text-to-SQL, NLU, structured reasoning). Implement RAG, prompt systems, evaluation and CI/CD for AI features, instrument and optimize model performance, and collaborate with product to deliver user-facing capabilities.

Develop, train, and deploy AI/ML systems, including deep reinforcement learning, explainability, adversarial learning, and interpretability solutions. Architect AI systems, design training datasets, implement research-based models, develop MLOps pipelines, and communicate results to technical and nontechnical audiences. Lead multidisciplinary projects, support proposals and conferences, mentor junior engineers, and collaborate across teams. The role requires strong Python development, deep learning expertise, research literacy, customer interaction, and an active or obtainable Secret clearance.

Develop and integrate LLM-powered capabilities into government software supporting warfighting training exercises. Prototype generative AI solutions, build production-quality Python and PyTorch software, evaluate emerging AI technologies, support architecture, and collaborate in an Agile/SAFe multidisciplinary team. The role includes mentoring technical contributors, engaging end users, participating in planning and reviews, and delivering capabilities on a 10-week cadence.

Develop, train, analyze, and customize supervised and reinforcement learning models; design training datasets; build MLOps pipelines; and communicate results to technical and nontechnical teams. The role requires Python and deep learning experience with PyTorch, TensorFlow, or JAX, plus eligibility for an active Secret clearance. Preferred experience includes RL frameworks, MLOps and DevOps tools, time series, satellite imagery, graph neural networks, and advanced RL algorithms.

Build and productionize LLM-based and agentic AI systems, transforming validated prototypes into reliable enterprise deployments. Design shared AI platform capabilities including agent infrastructure, evaluation harnesses, deployment patterns, and sandboxes. Partner with data engineering, security, infrastructure, and delivery teams to integrate governed enterprise data, establish standards, and ensure operational readiness. Document architectures, support clean handoffs, and evaluate emerging AI technologies for enterprise use.

Build production-ready AI applications, agents, automation, and developer tools for customer implementations. Develop AI-powered data conversion, testing, configuration, and workflow solutions using LLMs and AWS technologies. Collaborate with Implementation, Engineering, QA, and DevOps teams to integrate solutions into CI/CD pipelines, evaluate emerging AI capabilities, and optimize system reliability, accuracy, and scalability.

Configure, integrate, test, and support conversational AI solutions, including agents, workflows, prompts, knowledge sources, RAG configurations, and enterprise APIs. Translate business requirements into conversational experiences, use JavaScript for rules and orchestration, support UAT and deployments, monitor performance, troubleshoot issues, and optimize production platforms while applying governance, security, privacy, accessibility, and compliance standards.

Leads the strategy, architecture, governance, and delivery of conversational AI solutions. Designs conversation flows, prompts, integrations, knowledge sources, and escalation workflows using JavaScript, APIs, and LLMs. Establishes compliance controls, monitors performance, optimizes AI experiences, and drives continuous improvement. Provides technical leadership, mentorship, roadmap development, vendor collaboration, and stakeholder communication while ensuring scalability, security, accessibility, and regulatory alignment.

Designs, develops, and deploys enterprise AI and machine learning solutions, including predictive models, intelligent assistants, automation, anomaly detection, and natural language interfaces. Integrates AI capabilities with Azure, enterprise data platforms, Power BI, dashboards, semantic models, and reporting systems. Partners with data engineering and business stakeholders to build ML-ready data foundations, establish responsible AI practices, and align solutions with business goals, governance, security, and compliance requirements.

Owns the strategy, development, integration, administration, governance, and continuous improvement of enterprise business applications. Designs workflows, automations, APIs, AI-enabled solutions, data models, reporting, and integrations across Dynamics 365, Power Platform, HubSpot, Copilot, and Azure services. Gathers requirements, documents processes, supports Agile delivery, manages vendors, develops training, and improves operational efficiency through automation and AI.

Serve as the senior technical authority for AI-powered drug development applications, especially protocol generation and clinical trial execution. Architect and build production-grade agentic systems, define LLMOps, evaluation, observability, security, and GxP standards, and guide validated software development. Lead technical delivery, mentor engineers, assess models and tooling, and collaborate with clinicians, statisticians, medical writers, clinical operations, and product teams.

Lead the architecture and production deployment of AI and agentic systems for semiconductor metrology. Build LLM-based workflows grounded in tool data, manuals, recipes, and measurement outcomes; develop evaluation harnesses, uncertainty-aware ML, and on-premises inference. Establish engineering standards, testing, monitoring, CI/CD, security, and responsible AI practices while mentoring engineers and translating customer fab challenges into reliable AI solutions.

Develop generative AI solutions using Python, AWS, LLMs, chatbots, prompt and context engineering, RAG, vector databases, and agentic AI frameworks. Build coding prompts and contexts with GitHub Copilot, Claude Code, and Amazon Q Developer to convert business stories into production code and pull requests. Design autonomous and multi-agent workflows, memory and token-management systems, caching strategies, MCPs, and real-time AI decisioning features. Auto-lending experience and A/B testing expertise are advantageous.

Build the core AI intelligence layer for an energy infrastructure platform. Own technical strategy, architecture, production engineering, agentic workflows, data and geospatial pipelines, security, and observability. Translate energy procurement and project finance workflows into software, automate contracting and diligence, and iterate directly with users to achieve product-market fit. This founding role requires autonomous execution, rapid prototyping, technical leadership, mentorship, and comfort with startup ambiguity.

Build backend services, ontology models, retrieval and evaluation systems, and production LLM workflows for legal drafting and review. Own features end to end across PostgreSQL, OpenSearch, graph databases, and AWS, while improving performance and scale. Work directly with law firm customers alongside senior forward deployed engineers, contribute to TypeScript and React interfaces, and translate vague customer problems into data models, retrieval improvements, or software fixes.