Top AI Engineer Jobs

Engineer, operate, secure, and govern enterprise AI and automation platforms across development and production environments. Responsibilities include PostgreSQL and Redis administration, Linux and container infrastructure, high availability and disaster recovery, observability, incident response, CI/CD, Terraform-based infrastructure-as-code, identity integration, RBAC, security controls, audit support, documentation, and mentoring.

Build, deploy, support, and scale AI-enabled enterprise applications, automations, integrations, and internal tools. Translate business workflows into governed production solutions, establish reusable deployment and support patterns, and assist citizen developers. Collaborate across IT, manufacturing, engineering, quality, supply chain, finance, HR, and other functions on testing, security, documentation, adoption, monitoring, and handoff. Measure business value, troubleshoot applications, and guide responsible AI use.

Design, develop, and deploy enterprise-scale AI systems powering intelligent agents, copilots, and autonomous workflows. Build production-grade LLM, RAG, vector database, and agent orchestration solutions; develop APIs, SDKs, and platform services; and optimize reliability, latency, security, cost, and scalability. Lead initiatives through production, implement testing, CI/CD, monitoring, and governance, troubleshoot distributed systems, collaborate cross-functionally, contribute to architecture and documentation, and mentor or guide team members.

Lead AI workload characterization and storage architecture for large-scale inference, RAG, and training systems. Analyze performance across inference runtimes, Linux storage and networking, SSD hardware, and GPU infrastructure. Drive data-placement strategies, modeling, benchmarking, customer proofs of concept, standards engagement, cross-functional architecture decisions, and technical mentorship. The role focuses on integrating AI data paths with NAND, SSD, NVMe, and networked storage systems.

Designs and delivers enterprise-grade AI-first full stack applications for Master Data Management. Develops Java backends, React or Angular frontends, REST APIs, microservices, data models, and integrations. Leads architecture, debugging, testing, performance, scalability, code reviews, Agile ceremonies, and technical direction. Uses and evaluates AI coding tools, mentors junior engineers, and collaborates with product, UX, QA, and platform teams.

Build and ship production machine learning systems for content abuse detection, including data pipelines, classifiers, LLM evaluations, inference infrastructure, monitoring, and model evaluation. Optimize precision, recall, latency, cost, and throughput while partnering with engineering and data teams. Develop moderator intelligence features, build training and serving platforms, establish metrics infrastructure, and mentor teammates. The role requires end-to-end ownership in a startup environment, from messy data and feature engineering through deployment and production monitoring.

Design, build, and deploy secure production AI applications using LLMs, RAG, agentic workflows, external tools, and enterprise data. Develop reusable orchestration, retrieval, evaluation, and observability components; create data pipelines; optimize reliability, cost, latency, and scalability; and deploy cloud-native services on AWS. Collaborate with multidisciplinary teams and clients to prototype, evaluate, and transition mission-critical AI solutions into production.

Build and optimize high-performance LLM inference systems across data centers and edge devices. Develop inference engines, accelerate models on GPUs and AI hardware, design Kubernetes-based autoscaling architectures, and implement observability, load testing, and reliability frameworks. Improve latency, throughput, cost efficiency, and scalability across real-time and batch AI workloads while collaborating with hardware and cross-functional engineering teams.

The AI Developer will design and implement AI solutions tailored for federal healthcare, collaborating with teams to optimize healthcare data analysis and improve service delivery. Responsibilities include developing AI software, ensuring compliance with regulations, and maintaining data privacy.

Own and build the AI and automation layer for revenue operations. Identify and re-engineer high-value workflows across pipeline, forecasting, renewals, and churn using LLMs, AI agents, and automation frameworks. Partner with sales, CS, finance, data teams, and portfolio companies to deploy production systems, measure impact, and iterate to increase predictable revenue and retention.

Build and prototype state-of-the-art large language models and AI assistants, take services from ideation to production, fine-tune open-source models, use LLM platforms, analyze model statistics and projections, and collaborate cross-functionally in a fast-paced hybrid Palo Alto environment.

Provide SETA technical advisory support to government teams developing and evaluating AI-enabled applications. Assess LLMs, RAG, agentic workflows, orchestration frameworks, prompts, evaluation harnesses, and integration risks. Advise on latency, reliability, observability, guardrails, and system integration. Develop guidance, prototypes, and briefings; support test and evaluation activities and collaboration with engineers, product leads, and mission stakeholders.

Lead design and delivery of production-grade generative AI systems on Azure AI Foundry, integrating structured and unstructured data, implementing RAG/agent patterns, driving semantic architecture, ensuring governance/compliance, mentoring engineering teams, and partnering with stakeholders to embed AI capabilities into client-facing products.

As a Senior Software Engineer at Vanta, you will design and build internal tools for go-to-market teams, focusing on applications that enhance sales and customer engagement, employing modern tech and AI workflows.

Build production-grade infrastructure for GTM AI agents: harden prototypes, write evals and prompts, implement observability, own deployment pipelines on GCP, manage safe database access, monitor and improve agent fleet, and partner with GTM builders to scale reliability.

Lead design and implementation of GenAI and agentic solutions for an S/4HANA migration: build Joule assistants, deploy AI workflows on SAP BTP, integrate BDC data for RAG and agent experiences, implement responsible-AI controls, define prompt/model/observability architecture, and lead the AI workstream through sandbox, MVP, and production ramp with stakeholder collaboration and team mentoring.

Lead application engineering for an AI-powered email product across backend, mobile, and desktop platforms. Design reliable AI workflows involving orchestration, state, tool use, retries, fallbacks, and failure handling. Build APIs connecting AI capabilities to product experiences, make architectural trade-offs, establish observability and operational excellence, and lead a small team of senior engineers through hands-on technical mentorship and code development.

Leads application engineering for an AI-powered email product, owning backend, mobile, and desktop architecture. Designs reliable AI workflows with orchestration, state management, retries, fallbacks, and failure handling; builds APIs connecting AI capabilities to product experiences; and leads a senior engineering team through architectural decisions, reviews, mentorship, and production excellence. Partners with ML leadership to ship scalable, observable, secure, and reliable systems while balancing latency, cost, user experience, and maintainability.

Lead the architecture and development of reliable AI-powered email applications across backend, mobile, and desktop platforms. Design APIs and workflow systems supporting orchestration, state, tool use, retries, fallbacks, and failure handling. Lead a small senior engineering team, make architectural trade-offs, mentor engineers, and establish strong production practices including observability, monitoring, testing, scalability, security, and operational excellence. Partner with ML leadership to deliver robust, high-quality product experiences.

Own product areas for an AI-focused neobank across financial services such as insurance, payments, savings, investing, and travel. Define user problems, product direction, roadmaps, metrics, and launch plans; collaborate with engineering, design, data, operations, compliance, and business teams; simplify financial workflows; launch features; monitor performance; and iterate using data, feedback, research, and production insights.

Own product areas for AI stockbroking and broader financial services, defining user problems, product direction, roadmaps, success metrics, and launch plans. Collaborate with engineering, design, data, operations, compliance, and business teams to simplify financial products, ship features quickly, monitor performance, and iterate using data, research, feedback, and production issues.

Own product areas across AI investing and financial services, defining user problems, product direction, scope, metrics, roadmaps, and launch plans. Collaborate with engineering, design, data, operations, compliance, and business teams to ship customer-focused features. Use research, feedback, metrics, experiments, and production insights to improve products, balancing speed, usability, business impact, compliance, and operational complexity.

Own AI Finance product areas across insurance, payments, savings, investing, travel, and other financial services. Define user problems, roadmaps, metrics, scopes, and launch plans; collaborate with engineering, design, data, operations, compliance, and business teams; simplify complex financial journeys; launch features; monitor performance; and drive iterative improvements using data, research, feedback, and production insights.

Own quota-carrying technical sales for Ramp’s AI applications, agents, and infrastructure. Lead discovery, demonstrations, workshops, proofs of concept, ROI and total cost of ownership analyses, technical validation, objection handling, and deal strategy through close. Partner with account executives and internal teams while engaging engineering, IT, security, finance, and executive stakeholders. Help establish repeatable messaging, sales plays, qualification criteria, and go-to-market processes for Ramp’s emerging AI business.

Own Aleph’s shared AI foundation, including model selection and routing, proxy infrastructure, context management, tool design, and evaluation systems. Ship production agentic features using prompt engineering, caching, parallel tool calls, and subagents. Build observability for agent performance, collaborate with customers and finance experts to define quality standards, and drive adoption of reliable AI systems across product teams.