Top AI Engineer Jobs

Develop and deploy full-stack software applications using C#/.NET, Tungsten Automation, SaaS, AI/ML, OCR, and generative AI. Build intelligent document-processing workflows, classification and extraction solutions, dashboards, integrations, and custom applications connected to enterprise repositories and systems of record. Perform code migration, testing, CI/CD deployment, production support, version control, change management, and security management across an agile software lifecycle.

Design and build a multi-agent LLM framework and production LLM features (RAG, text-to-SQL, retrieval, prompts). Own architecture, testing, monitoring, and optimization for fleet/telemetry data in a fast-moving startup.

Design and deploy autonomous, multi-agent AI systems using agent orchestration frameworks, MCP, A2A, RAG, and knowledge graphs. Fine-tune and optimize language models for edge deployment, build evaluation and observability frameworks, and advance AI solutions from prototypes to production. The role requires cloud architecture experience, software development expertise, TS/SCI clearance with polygraph, and collaboration in ambiguous, rapidly changing environments.

Build and ship production AI products, including LLM coaching experiences, agentic workflows, retrieval and orchestration systems. Develop predictive, classification, ranking, and recommendation models; establish evaluation and quality processes; create trusted data pipelines and knowledge sources; and integrate AI with enterprise systems. The role requires strong ownership, safety-conscious engineering, evidence-based recommendations, and collaboration across Engineering, Product, and Data leadership.

Lead and mentor a San Diego engineering team while staying hands-on building production LLM systems (RAG, agents, document processing). Drive architecture, evaluate models and tooling, own end-to-end delivery, collaborate with clients, and create reusable internal tooling and best practices to improve team performance and client outcomes.

Develop and validate SystemC/TLM performance models for AI silicon subsystems, including SoC fabrics, memory systems, interconnects, and data movement paths. Analyze workloads, scalability, bandwidth, latency, congestion, and bottlenecks; correlate models with simulations and hardware measurements; build Python-based analytics, automation, dashboards, and validation frameworks. Partner with architects and design teams to provide performance-driven recommendations, lead technical initiatives, improve methodologies, and mentor engineers.

Leads development and delivery of projects using emerging technologies, including AI, blockchain, and IoT. Provides technical guidance and mentorship, evaluates technology solutions, collaborates with cross-functional teams, and aligns initiatives with organizational strategy. The role requires software development expertise, knowledge of architecture and design patterns, strong leadership, communication, and continuous learning.

Design and maintain scalable cloud data pipelines, ETL processes, and data warehouse solutions supporting insurance actuarial pricing, reserving, and reporting. Lead adoption of AI, Generative AI, Agentic AI, RAG, automation, and modern cloud data technologies. Collaborate directly with actuarial customers, reconcile data, define technical solutions, mentor engineers, and modernize on-premises pipelines for cloud environments.

Build and scale production agentic AI systems for a regulated fintech platform. Responsibilities include architecting AI infrastructure, improving context, memory and state management, developing evaluation systems, integrating AI features with TypeScript and Node.js, enforcing security and privacy controls, and establishing AI-native engineering practices. The role requires reliable, production-scale AI experience and comfort working in a fast-moving startup environment.

Design, build, and optimize agentic AI solutions, including LLM prompts, agent workflows, MCP tools, tool-calling frameworks, RAG systems, and context orchestration. Develop evaluation methods, integrate enterprise systems and APIs, support deployment and monitoring, and ensure solutions meet security, governance, compliance, and responsible AI standards. The role is client-facing and involves collaboration across product, architecture, and engineering teams.

Designs, builds, and evaluates agentic AI systems for healthcare workflows such as documentation, coding, denial management, and revenue cycle automation. Develops long-horizon agent behavior, evaluation frameworks, benchmarks, feedback loops, monitoring, and production safeguards. Partners with research, ML engineering, clinical, product, and engineering teams to prototype and deploy reliable, explainable, auditable AI systems using LLMs, machine learning, and agentic technologies.

Build and deploy AI/ML tools for GeForce NOW SRE operations, transforming production data, metrics, logs, and signals into incident root-cause analysis and operational forecasts. Lead LLM- and agent-based systems, develop large-scale data pipelines, optimize LLM workflows, and recommend sustainable AI platforms and architectures. The role also involves automation, Kubernetes, AWS cloud environments, monitoring, visualization, and production SRE practices.

Develop, fine-tune, deploy, monitor, and optimize NLP and large language models using transformer architectures. Build production-ready model applications and REST APIs, collaborate with product and data science teams, and improve reliability and scalability. The role involves applying frameworks such as PyTorch or TensorFlow, exploring agentic AI technologies, and deploying solutions with modern cloud and containerization tools.

Design, develop, test, and productionize AI agents for secure federal cloud environments. Responsibilities include prompt engineering, integrating generative AI platforms, optimizing performance and cost, validating outputs with statistical models and subject-matter feedback, and improving agent reliability. The role requires U.S. citizenship, an active Secret clearance, relevant education or equivalent experience, AI/ML engineering experience, LLM API expertise, Git proficiency, and full-time on-site work in the DC area.

Build and maintain scalable AI agents, pipelines, and services; improve infrastructure for running, evaluating, and monitoring agents; collaborate with product and security teams on AI-powered security features; and evaluate emerging LLM and agent technologies for product use.

Design, develop, and deploy AI solutions in production; lead AI pilot programs; integrate AI capabilities into existing systems; provide technical guidance; contribute to full-stack development; and optimize AI models for performance and scalability. The role requires expertise across programming, cloud platforms, APIs, databases, SAP, deep learning, NLP, computer vision, and DevOps, supporting financial auditability and transportation logistics for USTRANSCOM.

Own technical solution design, guide engineering teams, conduct design and code reviews, manage technical risks and dependencies, develop microservices, implement authentication and authorization, improve reliability and performance, and deploy containerized services.

Develop and operationalize AI, machine learning, NLP, predictive analytics, and generative AI solutions for a federal enterprise data platform. Build reusable AI/ML patterns, agentic workflows, prompt orchestration, MLOps pipelines, monitoring, evaluation, guardrails, and human-in-the-loop processes using Azure, Databricks, MLflow, and Python. Collaborate with engineering, security, governance, and business stakeholders to deliver scalable, trustworthy, governed, and production-ready AI capabilities.

Design, develop, and deploy production-grade multi-agent AI systems for financial pricing and hedging platforms. Build RAG pipelines, vector database integrations, planning and reasoning capabilities, resilient agent architectures, APIs, and microservices. Optimize systems for latency, throughput, reliability, and cost. Apply AI research to financial workflows, provide technical leadership, define AI strategy, and mentor junior engineers.

Leads hands-on design and delivery of production agentic AI systems for the NBA platform. Builds multi-step AI workflows, integrates LLMs, implements RAG and vector search, develops evaluation and reliability frameworks, and embeds AI-powered analysis into user-facing interfaces. Leads a small engineering team and contractors, partners with product and frontend teams, and ensures safe, auditable AI operation in a regulated healthcare environment.

Build, deploy, and maintain production AI agents, LLM integrations, REST APIs, data pipelines, and cloud workloads across ERP, CRM, and business systems. Translate operational needs into practical AI and automation solutions, manage deployments and monitoring, implement human-in-the-loop controls, and collaborate with technical and business stakeholders. The role requires strong Python, direct LLM API, Google Cloud, SQL, and production software delivery experience.

Develops and transfers AI research into real-world autonomous aircraft and vehicle systems. Evaluates multimodal perception models, builds prototypes, combines learned and classical computer vision methods, and optimizes capabilities for edge deployment. The role involves Python and C++ software, sensor fusion, model evaluation, robotics, and collaboration with researchers and autonomy engineers to deliver tested, documented production capabilities.

Build and operate production backend services supporting Supabase AI products, including the MCP server. Responsibilities include service architecture, deployments, scaling, authentication and authorization, observability, reliability targets, on-call incident response, rate limiting, capacity management, and API evolution. The role requires strong TypeScript or Go skills, production backend experience, distributed-systems debugging, and collaboration with Platform and SRE teams in a fully remote environment.

Design, build, and ship production LLM-powered features for Gusto Retirement. Implement and integrate LLMs (RAG, agents, prompting, tool loops), make pragmatic architecture decisions, collaborate with product, design, and engineering, write reliable well-tested code, and drive end-to-end execution and iteration.

Design, build, and operate large-scale, highly available Responsible AI cloud services. Lead projects, ensure production readiness and DevOps practices, integrate with Azure AI products, and collaborate with engineers, scientists, and stakeholders to deliver scalable, observable, and secure Responsible AI features and infrastructure.