Top AI Engineer Jobs

Design, deploy, and maintain production AI workflows and infrastructure. Build LLM-powered applications, multi-step agentic pipelines, serverless services, and integrations. Own deployments on Vercel/cloud, CI/CD, monitoring, secrets, and reliability. Enable cross-functional teams, debug escalations, prototype new tools, and drive AI initiatives end-to-end.

Lead architecture and build scalable, secure production services while championing AI-native development. Mentor engineers, influence technical direction, review PRs, and help evolve a legacy monolith to a modern stack in partnership with product and exec leadership.

Evaluate AI-generated coding interactions, assess quality of engineering judgments, provide feedback, and define standards for AI coding agents. Comfortable making subjective judgments about engineering quality.

Embed with customer teams to design and deliver AI/ML-driven solutions end-to-end: data ingestion, model integration, application logic, UI/UX, APIs, monitoring, and scaling. Collaborate on roadmaps, iterate rapidly in production, inform product/SDK direction, support pre-sales, and ensure reliability, performance, security, and compliance. Travel to customer sites (20–40%).

Lead design and implementation of AI-enabled ITSM and lifecycle automation: integrate ServiceNow and enterprise platforms, build reusable AI-driven automation skills, automate incident/problem/change workflows, improve data flows and observability, and deliver dashboards and playbooks to accelerate end-of-life lifecycle execution and reduce manual effort.

Design and implement Generative AI and agentic workflows to automate portfolio managers' processes, integrate data sources, perform UAT with stakeholders, monitor systems, and drive adoption through presentations and promotional materials.

Build and ship AI-powered automations and agents across business teams. Contribute to OUTGPT platform, implement integrations (APIs, SQL, Snowflake, Salesforce), deploy to AWS, and collaborate with stakeholders while receiving and giving mentorship.

Build and own production AI systems for legal workflows, including retrieval, document understanding, agentic LLM systems, evals, and data pipelines. Collaborate with engineers, lawyers, designers, and customers to translate legal judgment into reliable, usable product behavior.

Lead complex enterprise deployments of JetBrains Central, architect integrations with CI/CD and developer platforms, establish AI governance and observability, mentor FDEs, and shape product direction through customer feedback. Travel up to 20% and act as trusted technical advisor to customers and partners.

Lead quality engineering for PlayStation services by architecting scalable automated test frameworks (UI, API, integration, system), building CI/CD test pipelines, mentoring SDETs, defining test strategy and metrics, analyzing telemetry and production issues, and driving shift-left testing and quality improvements across teams.

Lead ML/AI quality strategy, design scenario-based test suites, and build scalable automation for backend services and ML inference. Use LLMs for test generation, validate model outputs, run functional/integration/performance testing, debug AI regressions, and integrate testing into CI/CD across cross-functional teams.

Verify chip-level and performance behavior of custom ASICs: design end-to-end test flows with architecture team, run simulation-based verification, implement UVM environments, use constrained-random and functional-coverage methodologies, and coordinate vendors/contractors.

Lead the design and evaluation of conversational AI systems from data preparation to model deployment, focusing on transforming unstructured conversations into actionable insights.

Lead design and evaluation of applied AI systems for conversational intelligence. Responsibilities include model architecture, evaluation frameworks, and collaboration with cross-functional teams.

Lead design and delivery of Agentic AI IAM and secrets management across hybrid environments. Build secure authentication/authorization, centralized secrets vaulting, automated rotation, governance, monitoring, and operational metrics. Partner with infrastructure, cloud, DevOps, application, and risk teams to implement Zero Trust, machine identity security, and AI agent safeguards while driving enterprise program execution and compliance.

Entry-level developer rotational role building AI-driven automations and low-code applications. Responsibilities include LLM orchestration, intelligent document processing (OCR + AI), API integrations, prompt engineering, testing, version control, data retrieval/transformation, and operating within regulated banking governance to improve business processes and customer experiences.

Design, implement, and maintain RAG pipelines and LLM integrations, build embedding generation and retrieval systems, develop REST API integrations, and refine Postgres schemas to support tactical intelligence data workflows in onsite, mission-focused environments.

Pre-sales technical lead for federal accounts who builds and delivers AI/ML and GenAI demos, designs compliant solutions (FedRAMP/DoD IL/NIST/FISMA), supports pilots/proofs, advises on data readiness and security, and partners with sales to drive federal adoption.

Lead AI adoption across Yahoo Mail by evaluating and operationalizing LLMs and coding agents, building integration frameworks, running proofs-of-concept, tracking adoption KPIs, training teams, and optimizing performance, reliability, and cost of AI tool deployments.

Design and operate petabyte-scale end-to-end data pipelines for autonomous driving: onboard upload, cloud preprocessing, dataset production, and model training input. Build data cleaning, annotation QA, lineage, versioning, metadata management, and high-throughput distributed processing. Optimize I/O, memory, transmission, and containerized deployments; support cross-team collaboration and algorithm iteration.

As an AI Tools Engineer at Counterpart Health, you will evaluate and deploy AI-powered tools, optimize workflows, ensure security compliance, and promote tool adoption across teams.

Design, build, and deploy production-grade AI/ML systems (LLMs and agents) for Defense and Intelligence clients. Implement scalable MLOps, integrate models with cloud and APIs, evaluate LLM performance, and design robust service-based architectures while meeting security clearance requirements.

Design and operate multi-petabyte, high-performance storage for large-scale AI/HPC clusters. Architect parallel filesystems and object stores, build Kubernetes-native storage operators, optimize RDMA/InfiniBand and NVMe-based data paths for 10–50 GB/s per node, implement multi-tier caching and prefetching, ensure observability and 99.9%+ uptime, and collaborate with ML/SRE teams while driving cost and performance improvements.

The Solutions Engineer will drive the adoption of AI-powered solutions in the electric utility sector, providing technical consulting and customer enablement. Responsibilities include gathering requirements, architecting solutions, leading RFI/RFP processes, and delivering training. Extensive travel is required for client engagement.

The Azure AI Applications Engineer will lead the development and implementation of intelligent applications using Azure AI services, while architecting solutions, mentoring junior engineers, and collaborating on project delivery across multiple sectors.