Top AI Engineer Jobs

Own the generation layer of Document Intelligence by designing prompts, schemas, validators, multimodal context pipelines, and quality evaluations. Build production LLM features that transform video, audio, PDFs, images, OCR, transcripts, and keyframes into validated maintenance entities. Balance model quality, cost, and latency, monitor regressions, and collaborate with processing and domain engineering teams.

Lead design, code, and architect scalable hybrid-cloud solutions for transactional and analytics workloads. Implement and review high-quality code, CI/CD pipelines, and AI/GenAI systems. Collaborate with development leads and stakeholders, present technical updates, prioritize work, and mentor team members to ensure robust, production-grade delivery and monitoring.

Build and lead the Site Reliability Engineering function across cloud, colocation, on-premises labs, and customer-facing services. Establish SLOs, observability, incident management, IaC automation, self-healing infrastructure, FinOps, capacity planning, and shared-storage architecture. Lead SRE and data center teams, hire and develop engineers, manage reliability and technical direction, and serve as the senior incident escalation point.

Owns Azure-based AI platform infrastructure, deployment automation, CI/CD pipelines, testing, observability, security controls, and release management. Builds containerized, infrastructure-as-code environments; implements monitoring, incident response, disaster recovery, and high availability; optimizes cloud performance and costs; and establishes secure platform engineering standards for AI-enabled applications. Collaborates with architecture, security, infrastructure, and development teams to scale reliable enterprise software delivery.

Build and operate the multi-tenant platform layer for a GPU cloud service. Responsibilities include Kubernetes-based orchestration, tenant isolation, platform APIs and tools, GPU cluster automation, infrastructure as code, reliability and observability partnerships, customer issue support, environment templates, rollout tooling, architecture reviews, and reducing operational toil.

Design, develop, and maintain enterprise AI applications, including conversational assistants, copilots, autonomous agents, RAG solutions, and workflow automation. Build scalable APIs and microservices integrating foundation models and enterprise systems. Apply prompt and context engineering, memory management, observability, governance, and performance optimization. Collaborate with cross-functional teams to identify automation opportunities and participate in Agile development, code reviews, CI/CD, and DevSecOps.

Design, develop, and apply AI and machine learning models for mission-critical defense applications. Build multimodal sensor, computer vision, NLP, and large language model solutions using Python and ML frameworks. Analyze large datasets, implement computational models and algorithms, collaborate with software engineers in agile teams, transition research into production-ready software, support proposals and technical reports, and present work to customers and professional audiences.

Designs, develops, modernizes, deploys, and supports AI-enabled applications and services. Establishes engineering standards for AI-assisted development, implements testing and CI/CD, ensures security, privacy, compliance, and Responsible AI requirements, and monitors application reliability. Collaborates across architecture, security, product, and operations teams, mentors engineers, and evaluates emerging AI technologies, including agentic AI and RAG architectures.

Designs, develops, modernizes, tests, deploys, and supports AI-enabled applications and services. Establishes engineering standards for AI-assisted development, implements CI/CD and observability practices, and ensures security, privacy, compliance, and Responsible AI requirements. Collaborates across architecture, security, product, and operations teams; mentors engineers; evaluates emerging AI technologies; and supports agentic AI and RAG architectures.

Build and deploy production-grade AI, ML, and intelligent automation solutions for Finance use cases. Design scalable systems, data pipelines, automation workflows, web apps, and intelligent agents; collaborate with Finance, Product, Engineering, and IT to deliver, monitor, and scale solutions that improve efficiency and controls.

Design, build, and operationalize production AI/ML systems on Google Cloud for federal defense programs. Lead Vertex AI, MLOps, data engineering, GenAI, security, and compliance initiatives; develop scalable pipelines and deployment platforms; implement monitoring, retraining, evaluation, and secure-by-design controls. Partner with mission stakeholders, mentor engineering teams, lead technical reviews, and document architectures, controls, and operating procedures.

The Applied AI Engineer will build and maintain AI agents, manage integrations and monitor performance, ensuring quality and continuous improvement of AI tools at Zello.

Build and scale machine learning systems for frontier AI data generation and evaluation. Responsibilities include efficient agentic evaluations, model routing, fine-tuning and serving smaller open-weight models, predictive task-difficulty modeling, LLM-as-judge measurement, and productionizing research prototypes. The role requires rigorous experimentation, statistical analysis, strong Python and software engineering, and end-to-end ownership of production ML or software systems.

Leads early supplier involvement for AI data center hardware, driving hardware co-design, DFM/DFT, liquid cooling, high-speed PCBAs, power delivery, rack integration, prototyping, cost modeling, production yield, and supply-chain risk mitigation. Partners with engineering teams and strategic suppliers to accelerate EVT/DVT, qualify alternate sources, and ensure scalable, high-quality manufacturing of high-density compute platforms.

Build and optimize foundational AI infrastructure for large-scale training, reinforcement learning, rollout, and inference workloads. Responsibilities include profiling bottlenecks, optimizing GPU and distributed execution, developing scheduling and load-balancing systems, modeling performance and capacity, improving fault tolerance, and creating benchmarks and observability tools. The role partners closely with AI researchers and systems engineers to improve throughput, reliability, utilization, latency, and cost across complex GPU clusters.

The Senior Software Engineer will build AI solutions for clinical workflows, collaborating with teams to enhance software architecture and mentor other engineers.

Design and build a production AI agent platform with RAG pipelines, document parsing, structured data mapping, and multiple agent types. Deploy and operate ML models on GPU infrastructure while ensuring scalability, security, compliance, observability, and performance in a multi-tenant cloud environment. Collaborate with Product Management and engineering teams, provide technical leadership through design and code reviews, and mentor engineers.

Build and deploy multi-agent AI systems that modernize applications and infrastructure across Goldman Sachs. Responsibilities include agent orchestration, human-in-the-loop learning, AI evaluation frameworks, observability, API and tooling integrations, and translating business needs into agentic workflows. The role requires production experience with LLM-based agents, distributed cloud systems, AI benchmarks, and agentic applications in areas such as data analysis, planning, optimization, or infrastructure observability.

Build and deploy production-grade multi-agent systems for firmwide application and infrastructure modernization. Responsibilities include agent orchestration, autonomous planning and execution, human-in-the-loop feedback systems, AI evaluation frameworks, observability, API and CI/CD integrations, and translating business needs into agentic workflows. The role requires strong software engineering and distributed cloud systems expertise, direct experience operating LLM-based agents, and experience applying agentic patterns to data analysis, observability, optimization, or planning.

Designs and implements AI and machine learning capabilities for cyber and intelligence workflows. The role translates mission-user processes into LLM-powered, agentic, or hybrid solutions; integrates AI with APIs, enterprise data, and mission platforms; and ensures reliable, secure production deployment in classified environments. Collaboration with analysts, operators, developers, and leadership is central. Required qualifications include Python, TS/SCI with polygraph, and relevant AI/ML or software engineering experience.

Develop and deploy production-grade AI/ML solutions on Microsoft Azure. Build secure data pipelines, prepare and validate datasets, support NLP and machine learning workloads, and implement MLOps practices including CI/CD, monitoring, troubleshooting, and model lifecycle management. Collaborate with engineers, analysts, and stakeholders in Agile teams while producing architecture, testing, security, and operational documentation for regulated government environments.

Leads end-to-end development of an AWS-based healthcare analytics platform across frontend, backend, data infrastructure, and AI capabilities. Sets architecture and engineering standards, writes and reviews code, mentors engineers, and partners with product, data engineering, and data science teams. Develops ETL pipelines, production machine learning services, generative AI features, and scalable analytics products while maintaining security, governance, privacy, and healthcare data quality standards.

Integrate and fine-tune existing AI models within production software, evaluate model performance, build evaluation frameworks, configure observability, develop Python AI features, and troubleshoot real-world performance. Collaborate with data science, engineering, the CTO, and cross-functional teams to improve products and identify AI innovation opportunities. This role focuses on applying existing models rather than designing new machine learning models.

Develop and maintain production AI and software solutions for alternative asset management workflows. Embed with business teams to identify use cases, build self-service AI capabilities, and deliver agentic workflows, retrieval pipelines, and evaluation tools. Use Python, SQL, Git, CI/CD, Docker, Terraform, and cloud deployment practices. Transition proofs of concept into production, lead concurrent projects, train technical and business users, and collaborate with stakeholders to define requirements and scope.

Develop and maintain production AI and analytics solutions using Python and SQL. Partner with financial business units to identify workflow improvements, build agentic and retrieval-based AI applications, and transition proofs of concept into reliable deployments. Use Git, CI/CD, Docker, Terraform, and cloud technologies while establishing evaluation tools, prompt libraries, and runbooks. Lead multiple projects, train technical and business users, and communicate requirements, scope, documentation, and changes to stakeholders.