Top AI Engineer Jobs

Design, deploy, and scale production AI applications, agentic workflows, LLM integrations, and intelligent automation across enterprise business functions. Build cloud-native services, retrieval and vector database solutions, evaluation and monitoring frameworks, AI guardrails, and reusable engineering components. Integrate AI with enterprise systems, optimize model performance and infrastructure, and support secure, governed, reliable production systems while collaborating with technical and business stakeholders.

Architect and own marketing data infrastructure, including Databricks pipelines, Gold Layer tables, semantic models, and documentation for self-service analytics. Lead cross-functional data initiatives, ensure reliability and quality, mentor engineers, and build production LLM and agent systems. Translate ambiguous business needs into scalable solutions, automate marketing and go-to-market workflows with AI, and deliver high-quality Python and SQL while overseeing AI-generated code.

Architect and own marketing data infrastructure, including Databricks pipelines, Gold Layer tables, semantic models, and ETL/ELT workflows. Build trusted data products for analytics, automation, and AI agents; translate ambiguous business needs into scalable solutions; lead cross-team initiatives; ensure reliability and data quality; mentor engineers and analysts; and develop, evaluate, and monitor production LLM and agent systems using Python and SQL.

Architect and own marketing data infrastructure, including Databricks pipelines, Gold Layer tables, semantic models, and trusted analytics foundations. Lead cross-functional data initiatives, translate ambiguous business needs into scalable solutions, ensure data quality and reliability, and mentor engineers and analysts. Build, evaluate, and monitor production LLM and agent systems, automate marketing workflows with AI, and deliver high-quality Python and SQL solutions supporting Marketing, Sales, and R&D.

Architects Samsara’s marketing data and AI foundation, including Databricks pipelines, Gold Layer tables, semantic models, and documentation for self-service analytics. Leads complex cross-functional data initiatives, translates ambiguous business needs into scalable solutions, mentors engineers, and ensures data reliability and quality. Builds and evaluates production LLM and agent systems, automates marketing and go-to-market workflows with AI, and delivers high-quality Python and SQL while supporting sales, marketing, and product teams.

Leads the architecture and delivery of large-scale AI/ML and GenAI solutions for global contact centers. Defines technical strategy, designs AWS-based APIs and infrastructure, establishes DevOps and CI/CD standards, manages model lifecycles, and ensures reliable production deployment. The role also mentors engineering teams, creates technical designs, evaluates emerging technologies, collaborates across teams, and supports troubleshooting and continuous improvement.

Owns the architecture, infrastructure, evaluation, and quality improvement of multi-channel AI marketing agents. The role develops measurable agent evaluation systems, feedback loops, SEO and GEO optimization, model strategies, and supporting data pipelines. It also involves hiring and leading the AI team as the startup grows, while partnering directly with the founder.

Build and productionize AI agents for customer support, voice interactions, and carrier onboarding. Develop primarily backend applications in Python and Java or Go, design databases and APIs, integrate frontend components, troubleshoot production workflows, conduct code reviews, and deploy services with Kubernetes. Collaborate with product, engineering, DevOps, US teams, and customers to deliver supply chain platform capabilities.

Owns technical outcomes for an AI value-stream pod from problem definition through production. Builds production Generative AI agents, workflows, and applications using Python and React; establishes AWS, Terraform, CI/CD, evaluation, observability, and human-review standards. Leads architecture, troubleshooting, vendor interfaces, platform stewardship, and design-council participation while mentoring engineers. Requires extensive software engineering experience, production AI ownership, and hands-on full-stack and cloud expertise.

Owns end-to-end development and production operation of Generative AI agents, workflows, and applications. Builds full-stack services with Python and React, deploys on AWS using Terraform and GitHub Actions, and implements evaluation, observability, security, compliance, and human-in-the-loop controls. Integrates enterprise systems, mentors junior engineers, troubleshoots production AI services, and partners with business users and vendors. The role is remote across the United States with travel as required.

Build enterprise AI platform components, semantic layers, and agentic workflows supporting investment research, portfolio management, risk, performance, and distribution. Develop reusable AI agents, orchestration patterns, retrieval and knowledge systems, and production governance capabilities. Partner with architects, engineers, product owners, and business stakeholders to deliver scalable solutions. Evaluate emerging AI technologies, establish engineering standards, promote Spec-Driven Development, and mentor teams on secure, observable, maintainable AI engineering practices.

Provides technical vision and architecture for AI-native and cloud-native systems. Leads development of scalable AI platforms, evaluation frameworks, monitoring, security guardrails, retrieval pipelines, and distributed systems. Drives architectural standards, strategic roadmaps, customer-critical initiatives, technical hiring, mentorship, incident resolution, and cross-functional influence. Requires deep expertise in LLM systems, cloud platforms, ServiceNow, Kubernetes, vector databases, responsible AI, and large-scale software engineering.

Leads software engineering teams developing production-grade AI/ML systems for industrial applications. Drives AI product innovation, architecture governance, modernization roadmaps, cybersecurity, scalability, reliability, and lifecycle management. Collaborates with engineering, data science, product, commercial, and field teams while mentoring talent and shaping technical strategy. Responsibilities include deploying predictive maintenance, diagnostics, forecasting, network optimization, LLM, RAG, and edge AI solutions across cloud-native and industrial environments.

Leads the architecture, development, integration, testing, deployment, and operation of complex AI, automation, and agentic solutions. Establishes reusable engineering patterns for RAG, AI agents, APIs, RPA, and human-in-the-loop systems. Drives enterprise integrations, governance, security, observability, CI/CD, production readiness, and operational support. Evaluates emerging technologies, provides architectural recommendations, coordinates cross-functional delivery, resolves complex technical issues, and mentors engineering teams.

Designs, develops, deploys, and supports AI-enabled workflows, automation, agentic solutions, and RPA integrations. Builds production-ready solutions using generative AI, LLMs, RAG, prompts, tools, and enterprise platforms. Establishes testing, evaluation, governance, security, monitoring, and operational support practices. Leads the AI and automation roadmap, evaluates proof-of-concept technologies, ensures production readiness, and mentors less-experienced engineers.

Leads software engineering teams developing production-grade AI and machine learning solutions for industrial environments. Responsibilities include identifying AI opportunities, guiding architecture and technology decisions, modernizing industrial products, ensuring scalability, cybersecurity, reliability, and maintainability, and collaborating across engineering, data science, product, commercial, and field teams. The role also mentors engineers, drives technical roadmaps, and oversees cloud-native ML systems, including predictive maintenance, diagnostics, forecasting, LLM assistance, and network optimization.

Designs, deploys, and optimizes agentic AI systems, LLMs, and SLMs for production. Responsibilities include model compression, hardware optimization, PEFT fine-tuning, agent orchestration, RAG infrastructure, vector search, automated data pipelines, validation, monitoring, and production integration. The role also mentors engineers, collaborates across technical teams, and communicates AI trade-offs to client stakeholders.

Designs, develops, deploys, and optimizes generative AI systems for customer missions. Responsibilities include end-to-end model development, data preparation, evaluation, deployment, prototyping emerging AI technologies, prompt engineering, and LLM/RAG product development. The role collaborates with program, product, architecture, and data teams, conducts independent research, mentors junior staff, and delivers scalable AI solutions in a collaborative environment.

Build and scale enterprise AI capabilities, including AI agents, automated workflows, retrieval-augmented generation pipelines, and prompt systems. Partner with business teams to translate workflows into technical solutions, prototype and deploy AI applications, administer enterprise AI tools, and support responsible AI governance in a regulated banking environment.

Build and scale enterprise AI capabilities by developing AI agents, automated workflows, retrieval-augmented generation pipelines, and prompt systems. Partner with business units to translate workflows into technical solutions, prototype and deploy AI tools, support embedded AI teams, administer enterprise AI platforms, and promote responsible AI practices in a regulated banking environment.

Build scalable Databricks Lakehouse architectures and ETL/ELT pipelines using Spark and Delta Lake across AWS, Azure, and GCP. Tune performance and cloud costs, integrate MLflow for AI model deployment, and implement CI/CD and Infrastructure-as-Code with Terraform or Databricks CLI. The role also enforces governance and security through Unity Catalog, supports regulatory compliance, and collaborates with clients, data scientists, architects, and engineering teams to deliver production-ready AI solutions.

Build and lead production cloud-native applications incorporating Generative AI, RAG pipelines, AI agents, LLM APIs, and prompt engineering. Design React or Angular interfaces, Python or Java REST APIs, and containerized Kubernetes deployments on Google Cloud. Contribute to CI/CD, testing, security, architecture, mentoring, and Agile delivery while governing AI quality, safety, and compliance.

Build and operate HedgeServ’s shared AWS platform for AI applications and agents. Responsibilities include platform architecture, infrastructure automation, production LLM and agent deployment, distributed-system reliability, identity and security controls, observability, model governance, incident response, cost optimization, and reusable engineering standards. The role partners with engineering, security, product, architecture, and business teams to deliver secure, scalable, reliable AI capabilities.

Own end-to-end delivery of core product features, build AI/ML pipelines for construction document and drawing analysis, and shape scalable frontend, backend, and ML architecture. Collaborate with co-founders and construction users, translate workflow needs into product improvements, establish engineering practices, and help grow the team. The role spans computer vision, LLM systems, frontend, backend, and production infrastructure in a fast-moving startup environment.

Design, develop, and deploy AI-native software products using multi-agent workflows, large language models, geospatial data pipelines, and cloud infrastructure. Build autonomous agents with tool use, structured reasoning, feedback loops, and reinforcement mechanisms. Develop scalable data ingestion systems, integrate secure enterprise environments, and operate production AI systems using DevOps practices. Contribute to engineering standards and leverage AI-assisted development tools for software design, testing, and documentation.