Top AI Engineer Jobs

Design, build, and scale Python backend services and agentic LLM workflows for a multi-tenant, regulated enterprise platform. Productionize LLM integrations (Bedrock), ensure security/compliance, implement async orchestration, optimize data layers (Postgres, vector search/RAG), and add observability, real-time eventing, and typed API contracts. Collaborate cross-functionally and drive architecture to production.

Build and improve production AI systems that extract structured data from legal documents into a household knowledge graph. Own and design evaluation suites, improve agent orchestration and tooling, create human-review workflows, and ship dependable, scalable changes to production while collaborating with domain experts and product.

Design, build, and operate an AI-first platform: ship LLM-backed features and agent orchestration, manage PHP production code, own AWS/ECS and serverless infrastructure as code, optimize MySQL/Aurora performance, implement observability and evaluation pipelines, and mentor the team while participating in on-call and incident response.

Design, develop, and deploy AI/ML applications for U.S. federal border security customers. Build optimized ML toolsets, maintain data governance, gather requirements from non-technical users, and support mission clients on-site in the Washington, D.C. metro area. Work includes cloud ML deployments (AWS/GCP) and configuring AI for law enforcement use cases; roles available at Associate, Senior, and Lead levels based on experience and capabilities.

Serve as a strategic advisor to AI and engineering teams to design, scale, and validate secure, trustworthy AI solutions. Define AI security strategy and roadmaps, run POCs/POVs, create standards and best practices, evaluate models and data pipelines for risks, apply analytics to drive security outcomes, and collaborate cross-functionally to translate technical findings into actionable recommendations.

Lead design, development, integration, and deployment of end-to-end AI/ML solutions for clinical settings. Collaborate with multidisciplinary teams, build CI/CD and MLOps pipelines, ensure regulatory-quality engineering, mentor junior engineers, define evaluation metrics, and translate technical results into actionable guidance for clinicians and stakeholders.

Lead design, deployment, and scaling of enterprise DLP across cloud, endpoint, SaaS, and network with a focus on AI-driven automation. Build automated classification, remediation, and workflows, integrate DLP with SIEM/SOAR/identity/cloud, develop policies for regulatory compliance, mentor engineers, and present program insights to leadership.

The HPC & AI Performance Engineer will execute benchmarking projects, analyze performance, and optimize HPC systems using scientific software and methods.

Build and secure AI-powered applications, agents, and integrations for company teams; define and own AI governance, inventory, monitoring, and DLP; teach safe AI usage and set company-wide guardrails; enable non-technical teams to adopt sanctioned, secure AI solutions with reliable backends, observability, and evaluative quality checks.

Lead design and rewrite of cloud-native core systems. Build scalable back-end services and front-end features, drive architecture and testing discussions, mentor engineers, and improve telemetry, performance, and reliability.

Build, operate, and scale cloud-native backend services and APIs for a high-volume vehicle marketplace. Embed AI-driven capabilities into features, use AI tools to accelerate development, participate in on-call rotations, improve reliability, and collaborate with Product, Data, Infrastructure, and SRE teams to deliver production-ready systems.

Lead architecture, build, and maintain scalable ML backend systems and pipelines. Deploy and operationalize data science models, mentor teams, engage stakeholders, ensure quality, compliance, and drive enterprise ML initiatives end-to-end.

Build and integrate AI/ML capabilities into Stord's logistics platform using Elixir services. Implement demand forecasting, intelligent routing, real-time predictions, anomaly detection, LLM APIs, and streaming data pipelines. Design distributed, fault-tolerant systems, optimize performance and costs, add observability and feature flags, and collaborate with product, data science, and ML teams to productionize AI features.

Lead strategy and technical execution of vulnerability management, offensive security, and AI-augmented defenses. Own full VM lifecycle, integrate scanning and asset systems, automate remediation, generate security insights, define KPIs/SLAs, advise executives, and mentor engineers to improve organizational security posture and compliance.

Lead enterprise strategy and engineering to defend against AI-powered threats. Design detection, prevention, and response architecture for adversarial AI, integrate controls into cyber tooling, build threat models, prototype defensive AI, and mentor teams while influencing leadership and vendor selection.

Lead the security design, architecture, and implementation for an enterprise AI platform. Define secure deployment patterns, embed security into CI/CD and operations, secure GenAI workflows, and lead customer security reviews and compliance activities for Fortune 1000 deployments.

Design, build, and deploy AI/ML capabilities (LLMs, RAG, embeddings, vector search) into secure cloud environments to accelerate cyber analyst workflows. Integrate models with APIs, monitor performance and drift, support retraining, perform experiments/AoAs, and provide stakeholder training and operational guidance in government settings.

Design, build, and operate a production generative/agentic AI platform for autonomous SOC workflows and multi-agent orchestration. Own end-to-end feature development, reliability, security, observability (OpenTelemetry), incident response, and technical documentation for high-stakes federal deployments.

Build and ship security features for an AI control plane: enforce enterprise policies, improve risk policy systems (deterministic and LLM-judged), integrate identity providers, and harden platform security. Collaborate across product and engineering, participate in on-call rotations, and own features from design through production and support.

Support development and implementation of AI governance, security policies, and controls. Integrate security tools across AI platforms, document AI use cases, and design privacy/compliance frameworks. Collaborate cross-functionally with data, engineering, and compliance teams to ensure responsible, secure AI adoption.

Design, build, and operate secure cloud CI/CD pipelines and automation (Ansible, Terraform, Packer). Lead identity governance for AI/non-human identities in SailPoint, automate access reviews/certifications, collaborate with InfoSec and platform teams to enforce controls, troubleshoot production issues, and drive centralized agent identity governance across cloud environments.

Designs and implements secure, fully automated CI/CD pipelines and identity controls for cloud deployments. Builds infrastructure-as-code (Ansible, Terraform, Packer), integrates identity and auth (AWS IAM, Azure/Entra ID, SailPoint, OAuth/OIDC/SAML/SCIM), and automates guardrails for non-human identities and agent workloads. Partners with architecture, InfoSec, and engineering to ensure compliance, observability, and scalable, secure deployments.

Design, build, and operate secure cloud CI/CD pipelines and automation (infrastructure as code) to support cloud deployments. Partner with InfoSec, architecture, and engineering to enforce identity and non-human identity governance, implement IAM integrations, secrets management, and automated guardrails. Troubleshoot production pipeline issues, develop tools to reduce errors, and research technologies to improve DevSecOps effectiveness.

Lead design and implementation of scalable SaaS applications with advanced AI features (semantic search, vector DBs, RAG, LLM orchestration). Translate business needs into technical designs, mentor engineers, enforce code quality and CI/CD, champion AI-assisted tooling, and drive architectural integrity and developer experience across the organization.

Research and develop machine learning algorithms and software prototypes for sponsor problems. Perform data collection, modeling, fusion, visualization, statistical analysis, and present technical reports. Collaborate with staff, supervise junior personnel and students, and integrate ML prototypes into research and pre-production environments.