Top AI Engineer Jobs

Design, fine-tune, optimize, and deploy enterprise-scale LLMs and NLP systems. Build distributed training pipelines, datasets, evaluation frameworks, RAG pipelines, embedding and vector search solutions, and agentic AI workflows. Optimize GPU training and inference, establish safety and responsible AI testing, collaborate across research and engineering teams, lead architecture reviews, and mentor junior engineers.

Designs and deploys enterprise conversational AI applications using LLMs, agentic workflows, multi-agent systems, RAG, and prompt engineering. Builds evaluation frameworks, SDKs, APIs, developer tooling, governance practices, and production infrastructure. Leads architecture and engineering standards, optimizes model performance and scalability, evaluates emerging AI technologies, collaborates across technical and business teams, and mentors engineers.

Designs, develops, and deploys enterprise-scale AI solutions, including LLMs, NLP, computer vision, multimodal systems, and RAG applications. Architects scalable training, inference, and MLOps pipelines; optimizes models and infrastructure; and establishes practices for evaluation, governance, security, monitoring, and lifecycle management. Collaborates across technical and business teams, mentors engineers, leads architecture reviews, and evaluates emerging AI technologies.

Own design and delivery of agentic Generative AI systems and infrastructure on GCP/Vertex AI. Architect GenAI workflows (LangChain, LangGraph, MCP), implement LLM evaluation and safety guardrails, build React/TypeScript frontends and cloud-native backends, and provide technical leadership and reusable AI patterns.

Design, build, and productionize agentic LLM workflows to improve clinical data extraction quality and reliability. Integrate AI capabilities into production pipelines, ensure scalability and cost-effectiveness, develop evaluation/monitoring for safe deployment in regulated environments, and lead technical design and delivery with cross-team collaboration.

Design and deploy AI/ML pipelines, LLM-integrated systems, and production AI applications for launch vehicle program management. Coordinate engineering changes, certification, supply chain activities, risk reduction, schedules, and cross-functional technical studies for New Glenn’s upper stage. Lead system-level trade studies and support cost, schedule, quality, and technical decision-making across engineering, production, and program management teams.

Lead the organization’s AI strategy, architecture, and adoption across a customer-facing insurance platform. Design scalable production AI, LLM, and agentic systems; establish LLMOps, evaluation, observability, governance, and security standards; guide build-versus-buy decisions; and champion AI-assisted development. Partner with engineering, product, and business leaders to redesign enterprise processes around AI while maintaining deterministic, auditable controls for policy, financial, and compliance workflows.

Support an enterprise sales pod by researching target accounts, identifying stakeholders, executing AI-assisted multichannel outbound, booking qualified meetings, and creating pipeline. Build AI agents, workflows, automations, dashboards, and GTM assets that improve research, personalization, prioritization, follow-up, and deal progression. Partner closely with account executives on stakeholder mapping, multi-threading, opportunity advancement, and ARR generation.

Lead GenAI strategy and implementation for government customers, serving as a technical advisor and hands-on builder. Identify high-value use cases, design architectures using the OpenAI API, develop prototypes, guide production deployments, manage customer and partner relationships, and provide feedback to product and research teams. The role also involves scaling deployment engineering practices, publishing technical resources, and collaborating across sales, engineering, research, and policy teams.

Build and operate Verizon’s enterprise AI platform, including model-routing services, agent SDKs, MCP integrations, evaluation pipelines, and DevSecOps automation. Own production services end to end across architecture, deployment, monitoring, reliability, incident response, and on-call operations. Develop multi-cloud Kubernetes infrastructure, APIs, token and cost management, data pipelines, canary deployments, and automated rollback systems using Python and related cloud-native technologies.

Lead the architecture, development, deployment, and scaling of AI-native solutions for federal clients. Build full-stack applications, RAG systems, semantic search, agentic workflows, and reliable data pipelines across structured and unstructured data. Guide embedded engineering teams, integrate solutions into legacy and classified environments, evaluate model performance, uphold responsible AI practices, and serve as the technical lead in client engagements. Travel may reach 100% based on mission needs.

Design, develop, and deploy agentic AI solutions using existing large language models. Build multi-agent workflows, engineer prompts, integrate AI capabilities through APIs and microservices, and evaluate output reliability. Collaborate with system engineers, software developers, and mission experts to automate intelligence reporting and dissemination workflows. Apply AWS, Python, orchestration frameworks, RAG, and MLOps practices while maintaining production-quality AI capabilities.

Build and operate governed, production-grade AI and LLM systems across Velsera’s products and business functions. The role covers multi-cloud model serving, MLOps and LLMOps, evaluation, governance, security, auditability, RAG, agentic workflows, and enterprise integrations. This senior individual contributor will establish reusable AI platform patterns, deliver customer-facing and internal capabilities, support regulated environments, and guide build-versus-buy decisions while collaborating with engineering, product, security, compliance, scientific, and commercial teams.

Design, develop, and deploy enterprise AI applications using LLMs, RAG, AI agents, vector search, document processing, and cloud-native technologies. Build scalable APIs, integrations, knowledge platforms, and Kubernetes-based services while contributing to architecture, CI/CD, security, testing, and observability. Collaborate with business and technical stakeholders to solve complex problems, evaluate emerging AI technologies, and mentor team members.

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 addressing privacy, security, memory, energy, and connectivity constraints. The role collaborates cross-functionally to deliver reliable on-device AI capabilities.

Design and implement multi-agent AI systems for marketing compliance workflows. Build production ML pipelines involving model fine-tuning, prompt engineering, retrieval-augmented generation, evaluation, monitoring, explainability, and feedback-driven personalization. Translate compliance rules into agent logic, ensure auditability across customer deployments, and collaborate with engineering, product, implementation, and compliance teams to deliver reliable AI features.

Lead the full development lifecycle, building scalable APIs and systems, mentoring engineers, and delivering impactful AI-driven software solutions.

Build and deploy production LLM agent systems that automate social commerce, marketing, creator, and ecommerce workflows. Responsibilities include discovery, architecture, development, integrations with advertising and commerce APIs, data pipelines, evaluation harnesses, cloud infrastructure, observability, adoption, and ongoing maintenance. The role requires end-to-end ownership, strong software engineering skills, experience with AI agents and third-party APIs, and understanding of performance marketing metrics and creator economics.

Build and deploy autonomous AI agents and LLM-powered applications for insurance brokerage workflows. Responsibilities include owning the development lifecycle, creating evaluation frameworks and benchmarks, monitoring production performance, optimizing and scaling systems, mentoring teammates, and promoting AI adoption across the company. Requires production software engineering experience, LLM application expertise, dataset development, benchmark creation, and TypeScript proficiency.

Design, build, and deploy scalable ML training and evaluation platforms for autonomous driving. Lead architecture and implementation of distributed, high-performance pipelines, drive cross-team prioritization, mentor engineers, and support recruiting and code quality to accelerate ML model development lifecycle.

Lead design and deployment of scalable ML platforms and tools, manage complex technical projects, mentor junior engineers, and collaborate across teams to enhance autonomous driving systems.

Build and deploy production AI agents for real customers. Responsibilities include orchestrating LLM pipelines, integrating APIs and tools, developing React interfaces, implementing RAG and document-processing systems, deploying serverless applications, and monitoring performance. The role requires strong Python or JavaScript skills, hands-on OpenAI and multimodal LLM experience, vector databases, agent frameworks, function calling, and full-stack integration. Applicants must be pursuing a Master's or PhD in a technical field.

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 and improve production generative AI systems that create editable motion from natural language. Responsibilities include model orchestration, structured generation, validation, compiler-backed quality gates, fine-tuning, post-training, synthetic data, evaluation, dataset governance, regression testing, and production reliability. The role requires hands-on ownership across LLM behavior, data pipelines, infrastructure, inference optimization, and product quality, with code generation, compilers, multimodal systems, or animation experience being valuable.

Design, train, test, deploy, and maintain AI/ML models (LLMs, RAG, agentic workflows). Build AI-enabled applications and secure data foundations on cloud (AWS). Implement MLOps/DevSecOps, event-driven integrations, and collaborate with engineers, architects, and product owners to deliver mission-focused Air Force solutions. Secret clearance required.