Top AI Engineer Jobs

Build LLM- and agent-based prototypes and production solutions, develop ML models where appropriate, prioritize reuse and scalability, collaborate with product, platform, and front-end partners, and support business development through technical delivery and client-facing engagement.

Architect and implement production AI/ML solutions (including generative and agentic systems), perform advanced analytics and experiments, define model evaluation and lifecycle standards, integrate solutions with engineering teams, and ensure compliance with banking regulations while mentoring peers.

The Principal AI Engineer designs and implements AI systems to solve business challenges, focusing on advanced analytics and collaboration with teams, while ensuring compliance and managing risks.

Architect and implement production AI/ML systems with emphasis on generative AI and agentic systems. Conduct data analysis, design experiments, build evaluation frameworks, integrate models into products, ensure compliance, and guide enterprise AI standards and best practices.

Develop and deploy GenAI and agentic AI solutions, including RAG pipelines, tool-calling agents, multi-agent workflows, data pipelines, and computer vision applications. Build production-quality Python and API code using cloud platforms, databases, and machine learning frameworks. Monitor model performance, evaluate AI quality, troubleshoot production issues, follow CI/CD and responsible AI practices, and collaborate with product, data, and engineering teams.

Founding Full-Stack AI Engineer to architect, build, and scale an enterprise digital account manager. Own end-to-end features, build integrations and data ontology mapping, develop intuitive UIs, and design agentic LLM solutions (memory, RL). Work directly with founders and customers to iterate rapidly and deliver enterprise-grade, scalable AI products onsite in San Francisco.

Designs and operates governance, security, deployment, monitoring, and cost-management frameworks for enterprise AI platforms. The role builds semantic models in Snowflake and Databricks, develops CI/CD and infrastructure-as-code solutions, enables multi-environment deployments, monitors AI reliability and usage, and implements auditability, responsible AI standards, RBAC, identity controls, and policy enforcement. It also partners with data and engineering teams to manage lineage, governed metrics, model operations, and cloud consumption across AI workloads.

Build and operate AI, MLOps, and deployment pipelines across customer and internal environments. Lead technical integrations, prototypes, demos, and production deployments across cloud, on-premises, and hybrid systems. Debug complex infrastructure and application issues, gather customer requirements, translate field insights into product capabilities, and create technical content. Partner with customers, Product, Engineering, Sales, and Business Development while helping establish forward-deployed engineering standards.

Designs, builds, and operates production-grade agentic AI systems at enterprise scale. Responsibilities include multi-agent orchestration, tool use, planning, memory, retrieval-augmented generation, model integration, evaluation, safety governance, observability, and workflow automation. The role also provides technical leadership through architecture ownership, code reviews, mentoring, and engineering standards for reliable production AI systems.

Build and operate production AI platform capabilities, including agentic and multi-agent systems, evaluation and monitoring frameworks, fine-tuning workflows, inference optimization, safety controls, APIs, and rollback mechanisms. Partner with data and infrastructure teams to deliver reliable, scalable AI services while balancing latency, cost, GPU utilization, and operational risk. Senior engineers also mentor teammates, review designs, and raise engineering standards.

Design and deploy production-ready LLM pipelines and agentic workflows, automate large-scale multimodal dataset pipelines, monitor and improve agent performance, and integrate AI agents with 3D scene composition. The role also involves process documentation, incident response, and continuous improvement, with collaboration across the engine team and company founders.

Lead architecture and delivery of complex AI harness and distributed software systems. Design agentic frameworks, AI/ML integrations, full-stack applications, cloud infrastructure, CI/CD pipelines, security controls, and enterprise integrations. Establish testing, performance, token optimization, and anti-hallucination practices; guide technical strategy, mentor engineers, and oversee projects from prototyping through deployment. The role is onsite in McLean, Virginia, includes occasional travel, and supports secure government and defense workflows.

Build and extend backend systems powering enterprise AI capabilities for data modeling, schema generation, documentation, governance, model orchestration, usage metering, and MCP integrations. Develop .NET microservices on AWS, integrate LLM providers, manage context and validation, and create reliable REST APIs for enterprise data workflows.

Leads the design, development, deployment, and support of secure, scalable full-stack applications using Java, Spring Boot, React, and TypeScript. Owns cloud-native microservices, APIs, event-driven Kafka architectures, observability, reliability, and technical architecture decisions. Mentors engineers and establishes quality standards while advancing AI-assisted development, agentic workflows, responsible AI practices, and engineering automation.

Architect, build, deploy, and scale production AI agents and platforms for legal workflows. Develop secure, reliable, and cost-effective AI solutions; prototype and evaluate agents; optimize performance; redesign business processes; and collaborate with Legal, Data, Security, Privacy, and Engineering teams. Contribute to AI training, governance, responsible AI adoption, and enterprise standards while translating technical capabilities into measurable business outcomes.

Leads the design, analysis, testing, and validation of cooled and uncooled turbine components, including blades and vanes. Develops innovative cooling schemes, improves turbine design systems, supports engine programs, and serves as a primary technical authority. Integrates AI and physics-based surrogate models with CFD and thermal analysis workflows. Leads cross-functional initiatives, communicates results to customers and senior stakeholders, develops proposals, and mentors junior engineers.

Lead technical sales for Deepgram’s Voice Agent platform, owning discovery, architecture, proofs of concept, technical evaluations, and customer production launches. Partner with account executives on enterprise deals, carry a growth quota, build demos and integrations, develop sales enablement, provide product feedback, and collaborate with telephony, contact center, and LLM partners. The role requires deep voice-agent expertise across telephony, orchestration, streaming pipelines, and speech and language models.

Build and deploy AI-powered tools for Rula’s business and clinical teams by observing workflows, rapidly delivering production software, measuring usage and impact, and enabling teams to operate tools independently. The role requires privacy-by-design practices, rigorous AI evaluation, customer-centric discovery, and integration with complex legacy systems. Staff engineers may optimize LLM costs and quality while creating reusable building blocks for future teams.

Leads enterprise AI platform adoption by embedding with IT teams, building agentic workflows and integrations, improving software delivery and operations, and measuring engineering efficiency. Applies AI across the SDLC, DevSecOps, observability, security, governance, and legacy modernization. Translates field learnings into reusable platform capabilities, influences senior stakeholders, mentors engineers, and serves as a technical escalation point without direct management responsibility.

Leads the technical vision, strategy, roadmap, and delivery of AI/ML capabilities across Cisco and Splunk products. Guides architecture, model development, production deployment, distributed training, and service integration while mentoring teams and influencing enterprise stakeholders. The role focuses on generative AI, large language models, deep learning, time series modeling, natural language processing, and scalable AI product development.

Build reference software codebases, support NGOs developing generative AI products, integrate diverse data sources, create experimentation tools, and deliver rapid cloud-based solutions. The role includes advising and mentoring partner organizations, conducting rigorous experiments, improving analytics infrastructure, organizing technical workshops, and visiting NGO sites to identify technical needs. Candidates need strong full-stack engineering, backend, data engineering, cloud, database, and generative AI experience.

Lead design and build of an end-to-end AI contract-review agent: prompt orchestration, RAG pipelines, fine-tuning, web UI and tooling, data/model infra, deployment (CI/CD/cloud), and product-driven features like assumption mapping and contract diagnostics.

Leads full-stack engineering initiatives involving distributed systems, cloud-native applications, AI agents, RAG pipelines, and real-time interfaces. Designs frontend and backend architectures using modern web frameworks, APIs, microservices, and enterprise integrations. Provides technical leadership across teams, establishes architecture and coding standards, mentors engineers, drives reliability and observability practices, and shapes platform roadmaps and technology strategy.

Design, develop, and deploy secure AI/ML and full-stack solutions for healthcare underwriting and benefits modernization. Build RAG pipelines, integrate LLMs and agentic AI capabilities, develop Python-based machine learning systems, and expose models through production APIs. Collaborate with engineering, architecture, product, data, and underwriting teams while ensuring model availability, security, performance, testing, monitoring, and responsible AI use.

Develop predictive models, quantitative analyses, data visualizations, and AI-powered applications using machine learning, NLP, generative AI, agentic AI, and LLMs. Build data pipelines and interactive applications, evaluate model performance and fairness, and collaborate with clients and teammates to deliver reliable solutions. The role requires software development practices, big data processing, and eligibility for a Public Trust determination.