Top AI Engineer Jobs

Build and maintain scalable data pipelines and datasets for ML/AI workflows; collect, clean, and preprocess structured and unstructured data; ensure data quality, availability, governance, and security; collaborate with data scientists and ML engineers; optimize storage and troubleshoot pipelines.

Architect and lead enterprise data platform strategy across ingestion, storage, processing, governance, security, and consumption. Design scalable lakehouse, warehouse, and streaming architectures; establish modeling, data quality, lineage, and catalog standards; optimize cost, reliability, disaster recovery, and capacity. Partner with ML, BI, product, and engineering teams, lead architecture reviews, mentor technical staff, and deliver architecture documentation and data product principles.

Designs, operates, and automates large-scale AI infrastructure for GPU-based training and inference. Responsibilities include distributed scheduling, high-performance storage and networking, observability, fault tolerance, cost optimization, developer tooling, security, and cluster lifecycle management across on-premises, cloud, and hybrid environments. Partners with ML research teams on capacity planning and maintains operational documentation. Requires extensive infrastructure or HPC engineering experience, GPU cluster expertise, systems programming, distributed training knowledge, and cloud ML platform experience.

Build and operate large-scale data infrastructure supporting AI training and evaluation pipelines. Responsibilities include data ingestion, transformation, quality assurance, lineage, versioning, reproducibility, and high-throughput delivery across modalities. The role requires operating petabyte-scale systems, applying distributed systems and data modeling expertise, supporting accelerator-based training, and following strong software engineering practices including testing, CI/CD, and code review.

Design, evaluate, and deploy applied AI and machine learning systems across language, vision, recommendation, and structured-data domains. Responsibilities include research experimentation, deep learning model training and optimization, production pipeline development, data quality tooling, responsible AI evaluations, and collaboration with product and engineering teams. The role also involves documenting findings, mentoring engineers, and influencing the AI roadmap.

Designs, trains, evaluates, and deploys reinforcement learning systems for complex decision-making problems. Responsibilities include developing reward functions, working with simulation environments, collecting experience at scale, training neural network policies on GPU clusters, and transitioning RL solutions into stable, safe production systems. The role also involves applying RL theory, collaborating through strong written and verbal communication, and contributing through shipped or published research. Preferred areas include RLHF, multi-agent or hierarchical RL, robotics, control systems, and autonomous driving.

Optimize large-scale AI training and inference systems for throughput, latency, and cost. Responsibilities include GPU kernel optimization, distributed system tuning, memory and compiler optimization, workload profiling, model compression, production-scale LLM inference, measurement-driven debugging, and collaboration with cross-functional teams. The role also involves code and design reviews, mentoring junior engineers, and shipping reliable production solutions.

Build and operate petabyte-scale data infrastructure for AI training and evaluation. Responsibilities include multimodal ingestion, transformation, deduplication, quality assurance, dataset versioning, lineage, high-throughput data loading, labeling workflows, privacy enforcement, observability, storage optimization, and collaboration with ML teams. The role requires strong data engineering, distributed systems, software engineering, and AI workload experience.

Build and operate petabyte-scale data pipelines for AI training, evaluation, and continual improvement. Responsibilities include multimodal data ingestion, cleaning, deduplication, labeling, dataset versioning, lineage, privacy enforcement, high-throughput GPU data loading, observability, storage optimization, and data quality management. Collaborate with ML researchers and engineers to improve model training efficiency and reproducibility.

Profile and optimize AI training and inference systems for throughput, latency, memory efficiency, and cost. The engineer will tune distributed training, GPU workloads, model compression, attention, LLM serving, data pipelines, and compiler-level optimizations. Responsibilities include developing benchmarks, regression frameworks, performance playbooks, and hardware evaluations while collaborating with ML and platform engineering teams. The role requires strong Python and C++ skills, deep knowledge of distributed systems and parallelism, and experience optimizing deep learning workloads on modern GPUs.

Leads applied AI engineers and data scientists while owning AI solutions from business problem framing through production deployment, monitoring, retraining, and impact measurement. Establishes ML and MLOps standards, responsible AI governance, technical roadmaps, and build-versus-buy strategies. Partners with business, data engineering, and BI teams across restaurant operations, ordering, supply chain, and guest experience. Guides generative AI, forecasting, computer vision, and intelligent automation initiatives and communicates outcomes to senior leadership.

Architect and scale high-cardinality observability and telemetry infrastructure for AI cloud platforms. Build Kubernetes exporters, eBPF diagnostics, hardware and network monitoring, dashboards, alerting, and GPU/network utilization billing pipelines. Establish telemetry standards for AI workloads, support proactive remediation, collaborate with GPU and scheduling teams, lead architecture reviews, and mentor engineers. The role requires expertise in Go, Kubernetes, Prometheus/OpenTelemetry, Linux performance tuning, eBPF, AI hardware metrics, and large-scale cloud or HPC telemetry systems.

Architects and operates high-performance storage infrastructure for AI training and inference. Responsibilities include developing Kubernetes CSI drivers, integrating GPUDirect Storage, optimizing NVMe caching and I/O performance, supporting RDMA and high-speed networking, monitoring storage systems, managing quotas and multi-tenancy, and mentoring engineers.

Lead the architecture and implementation of AI workload scheduling and orchestration for a large-scale NeoCloud platform. Build batch and gang scheduling, admission control, queueing, topology-aware placement, GPU sharing, and multi-tenancy capabilities using Kubernetes technologies. Optimize NVLink and InfiniBand utilization, integrate scheduling with hardware and storage systems, improve reliability in HPC environments, resolve contention and deadlocks, and mentor engineers while guiding architectural decisions.

Build modern data and AI analytics solutions using ThoughtSpot, Snowflake, semantic data layers, and governed data assets. Develop ETL processes, AI prompts, intelligent dashboards, and interactive analytics experiences for actuarial teams. Partner with business and analytics stakeholders to support pricing, reserving, underwriting, and portfolio decisions while surfacing trends, explaining business drivers, and enabling actionable recommendations.

Modernize enterprise Java applications and build secure, scalable cloud solutions across AWS and GCP. Lead infrastructure standards, Infrastructure as Code, CI/CD, observability, deployment automation, and production readiness. Develop GenAI and agentic AI solutions using Vertex AI and Google ADK, including development agents, test agents, and workflow automation. Partner with business, architecture, security, and engineering stakeholders, while mentoring engineers and advancing reusable enterprise engineering practices.

Develop backend systems for 3D AutoReview, including geometry parsing, model analysis, quality detection, rule systems, scalable APIs, and AI-assisted review workflows. Collaborate with backend, frontend, product, and machine learning teams to deliver production features. Build extensible systems for custom 3D rules and reference data while experimenting with CAD, machine learning, and large language model technologies.

Lead development of ServiceNow solutions enhanced with Generative AI, AI agents, Graph RAG, and Agentic RAG. Responsibilities include building ServiceNow applications, workflows, integrations, RAG pipelines, knowledge graphs, and intelligent agents using LangGraph, LangChain, and Neo4j. The role requires Python and JavaScript development, enterprise data integration, security governance, testing, production troubleshooting, and collaboration with technical and business stakeholders.

Develops, integrates, and sustains Palantir and AI data capabilities across enterprise repositories. Responsibilities include maintaining semantic layers, ontologies, schemas, vector indexes, retrieval pipelines, data quality and security controls, ingestion pathways, BI products, documentation, troubleshooting, and technical training. The role requires strong SQL and Python skills, Palantir Foundry experience, AI/ML retrieval or generative-AI expertise, enterprise data integration, and an active Secret clearance.

Designs, builds, deploys, and operates production AI/ML and LLM systems. Responsibilities include developing RAG pipelines, fine-tuning and evaluating models, managing end-to-end MLOps lifecycles, establishing monitoring and governance practices, mentoring engineers, and leading complex cross-functional client and internal initiatives.

Designs, builds, tests, deploys, documents, monitors, and supports AI-enabled automation solutions using Microsoft Power Platform and AI endpoints. Integrates systems through REST APIs, custom connectors, and webhooks; develops reliable prompts and guardrails; identifies enterprise automation opportunities; produces performance and adoption reporting; and ensures alignment with security and quality standards. Requires strong software engineering, business acumen, executive presence, and the ability to manage initiatives across business and technology teams.

Develop end-to-end AI and machine learning software solutions for mission-critical systems. Responsibilities include full-stack LLM development, RAG implementation, model selection and registry management, inference engine integration, REST API and React development, and DevSecOps CI/CD pipeline management. The role also supports digital and systems engineering modernization, considers broader operational ecosystems, and applies innovative AI technologies. Candidates must have relevant software development experience, a STEM bachelor’s degree, and the ability to obtain a Secret clearance.

Design, develop, and optimize autonomous AI agents capable of reasoning, planning, tool use, and multi-step execution. Build agent frameworks, human-AI collaboration mechanisms, monitoring and evaluation systems, and production integrations. Apply large language models, prompt engineering, reinforcement learning, planning algorithms, workflow orchestration, and system design. Collaborate cross-functionally while researching advances in agent technology and improving reliability, accuracy, safety, and efficiency.

Architect and deploy AI-powered compliance solutions using agentic workflows, Python, Claude, n8n, and MCP. Build automation for alert screening, case narratives, regulatory summaries, EDD reporting, analytics, and audit documentation. Own governance, risk classification, data privacy, audit logging, human oversight, and regulatory controls. Train compliance teams, create reusable runbooks, drive adoption, and measure automation impact across global financial crime and compliance operations.

Architect and deploy production AI-powered HR workflows using agentic systems, automation tools, Python, Claude, MCP, and HR system integrations. Own solution design, governance, risk controls, audit logging, privacy compliance, deployment, documentation, and adoption. Build integrations across Workday and other HR platforms, establish human oversight, track business impact, and train People team members to maintain and extend the systems.