Top AI Engineer Jobs

Build and maintain secure RESTful and event-driven APIs and MCP server implementations to allow AI agents to interact with enterprise platforms (ServiceNow, Okta, ERP). Implement authentication (OAuth2/OIDC), API gateway policies, audit logging, and monitoring. Collaborate with AI and governance teams, document integrations, and ensure secure, reliable, auditable agentic workflows.

The Data/AI Cloud Engineer will implement AWS data services, build ETL pipelines, optimize databases, and support data-driven solutions collaborating with technical teams.

As a Senior Engineer 2, you'll lead complex AI system designs, oversee team quality, integrate LLM APIs, mentor junior engineers, and collaborate across teams to enhance the AI Agentic Platform.

Lead R&D on LLM training and inference optimization, train and evaluate large-scale models on Tenstorrent hardware, improve performance via techniques like speculative decoding, quantization, kernel fusion, flash attention and distributed training, diagnose system bottlenecks, and translate research into scalable production solutions through cross-functional collaboration.

The AI Automation Engineer will apply AI in finance operations to automate processes like AP, AR, and reconciliation, ensuring compliance and performance measurement.

As a Missions Software Engineer, you'll collaborate with defense customers to integrate and operationalize vehicle intelligence products, tackling software engineering, hardware integration, and project execution challenges.

As a C++ Software Engineer, you will design high-performance backend systems for autonomy products, tackle complex engineering challenges, and collaborate with teams to deliver robust solutions for defense applications.

The Lead Software Engineer will design and implement backend systems in C++, tackle autonomy challenges, and ensure robust software solutions meet real-world operational demands.

Deploy and integrate AI-enabled SOC solutions across customer environments: design cloud-native ingestion pipelines, integrate SIEM/XDR/SOAR and tooling, implement automation and AI workflows, improve analyst experience, troubleshoot production issues, and provide technical leadership and field-facing delivery.

You will build and maintain AI-native application features, working with a team of AI and backend engineers to ensure quality and innovation.

The role involves architecting scalable backend systems for real-time evaluations, integrating with streaming data, and building intervention layers for AI applications. You'll design frameworks and operationalize vector databases while ensuring low-latency service performance.

Build and improve production AI/data pipelines, design model evaluation workflows and Jupyter analyses, add observability to AI systems, experiment with models/retrieval/prompt architectures, integrate and abstract AI APIs, and help define inference serving, fine-tuning, and dataset-generation infrastructure.

Embed with Finance to design, build, and operate production AI systems that integrate with ERP and data platforms, enable conversational and retrieval-based workflows, automate financial quality controls, surface operational intelligence, and support executive reporting. Own end-to-end delivery from discovery through deployment, monitoring, evaluation, and enablement while ensuring governance, compliance, and stakeholder adoption.

Lead design and delivery of cloud-native, event-driven platforms and applications. Drive full lifecycle development, champion engineering best practices, implement production-ready solutions, and build AI/LLM capabilities like RAG, embeddings, and model CI/CD across multi-cloud environments.

Build and ship production AI/LLM-powered workflows and platform services that operate over sensitive enterprise and government data. Design context/grounding, permissioning, orchestration, tracing, evals, and runtime visibility. Work across backend services, APIs, async workers, data pipelines, and product surfaces, owning reliability, auditability, and performance.

Design, build, and operate scalable, highly available database and data-serving infrastructure for production and AI workloads. Scale relational, analytical, and vector stores; optimize latency, throughput, and reliability; implement partitioning, sharding, replication, caching, query optimization, observability, backups, and disaster recovery. Partner with AI and engineering teams to support embeddings, vector search, retrieval workflows, analytics, and low-latency data access while building automation, tooling, and best practices for database operations.

Develop and implement compiler optimization algorithms for deep learning workloads, analyze networks, debug and profile GPU performance (CUDA), define public APIs, and deliver ahead-of-time and just-in-time compilation techniques for NVIDIA GPUs to improve inference performance, build times, and memory efficiency.

Contract SRE to build, operate, and automate colo, on‑prem GPU clusters, and cloud infrastructure. Own provisioning, IaC (Terraform/Ansible), observability (Prometheus/Grafana or DataDog), incident response, runbooks, and customer-facing platform reliability.

Build and productionize backend-first AI capabilities for a FinOps intelligence platform: design backend services, implement AI features (anomaly detection, recommendations, agents), create evaluation/rollback/monitoring systems, and collaborate with product to ship reliable, cost-efficient, production-ready features.

Design and implement production-grade backend services and platform APIs in Go. Build Kubernetes-native components (controllers, operators, CRDs, webhooks), integrate with CI/CD, observability, and security systems, and apply AI/agentic programming to automate platform workflows and improve developer productivity. Troubleshoot distributed systems and participate in architecture reviews.

Embed with client engineering teams to operationalize LLMs and retrieval systems across multi-cloud environments. Build agentic solutions using Vertex AI Agent Builder and ADK, connect agents securely to BigQuery/Spanner via MCP, implement streaming pipelines (Dataflow) for real-time inference, and manage model training, tuning, deployment, and evaluation on Vertex AI. Provide consultative guidance, cross-functional collaboration, and clear stakeholder communication to ensure business value from Generative AI solutions.

Lead end-to-end delivery of AI-powered applications: define requirements with clients, translate business needs into technical plans, coordinate cross-functional engineering teams, leverage AI tools for prototyping and delivery, track progress, remove blockers, and drive rapid iteration and continuous improvement.

Lead design, build, and validate end-to-end advanced AI systems (LLMs, agentic/multi-agent, distributed). Run hands-on evaluations, benchmarks, and prototypes; integrate emerging AI into cloud/hybrid/edge environments; ensure production rigor; provide technical leadership, architecture reviews, and convert prototypes into reusable frameworks and scalable solutions.

The AI and ML Engineer will develop and maintain AI/ML models, collaborate with teams to deliver solutions for national security, and apply algorithms.

Design, build, and scale production AI systems and cloud infrastructure. Architect multi-agent flows, implement RAG pipelines and semantic search, manage vector DBs and preprocessing, and deploy secure AI microservices in cloud. Collaborate with cross-functional teams and enforce security/privacy best practices for LLMs.