Top AI Engineer Jobs

Design and build backend infrastructure for agentic AI products: orchestrate low-latency agent runtimes, scale RAG/vector search systems, develop large-scale evals and testing pipelines, productionize multi-tenant AI microservices with observability and cost/performance optimizations, and collaborate with modeling teams to deliver secure, scalable enterprise AI features.

Design and build safe, auditable agent and autoresearch infrastructure: closed-loop evaluation, isolation and permissioning, experiment orchestration, and large-scale inference/training pipelines. Enable autonomous research loops, evaluator and confidence machinery, and agent runtime features (orchestration, sandboxing, tool frameworks, memory, retrieval). Work end-to-end with engineers to deploy trustworthy LLM-agent capabilities against production systems.

Build and maintain enterprise AI governance components (LLM gateway, Agent Firewall) across full stack (Go backend, React frontend). Ship features, harden security, integrate LLM provider APIs, collaborate with product, partners, and enterprise customers, and contribute to open-source tooling.

Lead full-stack product engineering for AI-powered healthcare features. Build frontend experiences, backend services, APIs, relational data models, and AI workflows. Prototype, ship, and iterate production systems; improve AI via evaluation and product signals. Ensure reliability, privacy, observability, and operational readiness while collaborating with product, design, customers, and engineers.

Design and implement advanced ML models (reinforcement learning, neural nets, metaheuristics) for logistics optimization, predictive analytics, anomaly detection, and real-time decision systems. Lead architecture design for scalable, secure AI systems, mentor teams, align technical roadmaps with defense requirements, and conduct feasibility studies and technology evaluations for contested and uncertain logistics environments.

Lead ownership of the Salesforce platform with emphasis on CPQ and Billing: design and deliver Apex/LWC/Flow solutions, manage integrations (REST/SOAP, platform events, middleware), run CI/CD, enforce development best practices, mentor engineers, and apply AI-assisted tools to accelerate delivery.

Design, simulate, and characterize RF front-end systems and RFICs for space and ground deployments. Derive specifications, develop RF/microwave circuits, MMICs, boards and packages, validate prototypes, support automated test development, and collaborate with systems, firmware, and manufacturing teams for production testing.

Develop, integrate, and deploy software and automation (including AI/LLM-assisted workflows) to reduce cyber risk for Navy customers. Clarify mission needs, design solutions, implement APIs and data integrations, document technical work, align with RMF and secure engineering practices, and collaborate in small Agile teams.

Design and guide enterprise cloud and AI infrastructure strategy, lead large-scale cloud migrations, define architectures and CI/CD pipelines, implement monitoring and security, produce design documentation, support L3 troubleshooting, and collaborate with product and engineering teams to operationalize DevOps processes.

Lead cloud migration, AI platform adoption, and operational optimization. Design and automate cloud infrastructure (IaC, CI/CD), monitor and troubleshoot systems, guide security and architecture decisions, and document/educate teams to ensure scalable, redundant cloud/AI deployments.

Develop and deploy applied AI systems: integrate models, build inference and evaluation pipelines, optimize performance for edge and adversarial conditions, create production-quality data and serving software, prototype with mission teams, and ensure reliability through testing, monitoring, and benchmarking.

Design, integrate, and deploy AI-enabled automation and analytics to reduce cyber risk for Navy missions. Engage stakeholders to shape solutions, implement services/APIs and data integrations, document security-aligned designs, and follow RMF and secure engineering practices while working in classified or controlled environments.

Design, execute, and operationalize fine-tuning workflows for large language models using supervised, preference-based, and reinforcement learning approaches. Build and operate distributed training pipelines, construct datasets, run rigorous evaluations, and collaborate with cross-functional teams to deliver production-grade LLM solutions while mentoring junior engineers.

Optimize throughput, latency, and cost for large-model training and inference across the stack, from kernel and compiler-level work to distributed system tuning. Profile and instrument GPU and distributed workloads, apply model-parallelism and memory-management strategies, and implement model-compression tradeoffs. Collaborate with cross-functional teams, perform code and design reviews, mentor junior engineers, and ship production-quality ML infrastructure improvements.

Own TLDR's GTM systems end-to-end: HubSpot administration, attribution, pipeline data integrity, automations, system evaluations, and monitoring. Build dashboards and alerting, diagnose recurring issues, decide between quick-fixes or re-architecture, ship automations and agent workflows, and document operational runbooks. Partner with Applied AI to apply AI to GTM problems like attribution.

Design, build, and integrate AI-powered applications (agents, assistants, monitoring) using Python and LLMs. Develop cloud (AWS) solutions, APIs, automation, and HPC data pipelines. Support DevOps/CI/CD, monitor system performance, troubleshoot integrations, and collaborate with research and engineering teams to evaluate and document AI solutions.

Design and build production-grade React/TypeScript user interfaces for AI-enabled supply chain applications. Own UX concept through implementation, integrate front-end with APIs, models, agents and enterprise systems, and ensure security, accessibility, performance, and observability while partnering with AI engineers and stakeholders.

The Data Engineer will develop and maintain AI context data capabilities, create data pipelines, ensure operational excellence, and follow compliance protocols. Responsibilities include data ingestion, retrieval quality improvements, and semantic layer implementation for AI integration within healthcare organizations.

The Lead AI Forward Engineer designs AI solutions, guiding implementations for scalability and compliance, while mentoring teams and shaping technology strategy.

Senior full-stack engineer to design and deliver enterprise web applications for banking using AI coding agents. Own end-to-end systems (Angular frontends, Java/Spring Boot microservices, relational/NoSQL data), integrate agentic workflows into CI/CD, validate AI-generated code, and coach junior engineers. Must apply banking domain knowledge and drive secure, scalable, production-ready solutions.

Lead design, development, deployment, and maintenance of large-scale ML systems and pipelines. Provide senior technical consulting, resolve complex ML and governance issues, plan end-to-end model integrations, and document implementations for performance optimization.

Join as a founding full-stack engineer to build an AI-native enterprise platform (agents, connectors, RAG, workflows). Own end-to-end product areas, design reusable primitives, ensure production readiness, work directly with customers, and shape roadmap and engineering practices.

Design, build, and evaluate managed AI agents embedded with business teams: translate processes into requirements, implement safe reliable agents, define evaluations and human-in-the-loop checkpoints, build reusable patterns, pilot and lead acceptance testing, and partner with data scientists and stakeholders.

Design, build, and scale AI-enabled cloud infrastructure including RAG pipelines, vector DBs, and multi-agent flows. Implement IaC (Terraform/ARM), Kubernetes (AKS/EKS), containerization, automation (Python/PowerShell), cloud security controls, and collaborate with cross-functional teams to deploy production LLM workloads.

Design, prototype, and deliver AI-enabled products, applications, APIs, and intelligent workflows. Build scalable Azure-based solutions using LLMs, agents, data pipelines, and CI/CD while ensuring security, governance, monitoring, and responsible AI practices. Collaborate with business and engineering stakeholders to define requirements, experimentation, and evaluation frameworks to drive measurable business value.