Top AI Engineer Jobs

Build end-to-end AI-native features: responsive React/TypeScript frontends, Python/Rust backends, cloud integrations (AWS/GCP), databases, CI/CD, observability, and optionally LLM/RAG/embeddings-based AI features while improving reliability, performance, security, and cost.

Design, develop, and deploy machine learning and deep learning models; build and maintain production ML pipelines; preprocess and validate large datasets; implement APIs and integrate models into systems; monitor, optimize, and retrain models; collaborate with cross-functional teams to translate business needs into AI solutions.

Participate in a two-year international rotation program across R&D teams, developing AI and software solutions for network testing and communications technologies. Build clean, tested Python code; work with LLMs, APIs, orchestration frameworks, and agentic AI; apply ethical and safe AI practices; collaborate in Agile teams; and rapidly learn new technologies while contributing to product development and issue resolution.

Build and maintain Go-based backend services and APIs on AWS for agentic AI workflows, including metadata suggestions, automated ownership reassignment, system deprecation, and agent/MCP inventory. Design database schemas and queries in PostgreSQL and MongoDB, integrate asset discovery tooling, map dependencies and user journeys, and refactor legacy code safely. Deliver tested features through Agile/Scrum while collaborating with program and customer teams.

Build and launch AI-powered prospecting agents, SDR tools, lifecycle automations, onboarding flows, and internal products. Integrate CRM, sales, and marketing platforms using workflow tools, APIs, scripts, and LLMs. Own lead scoring, routing, SDR-to-AE handoffs, and lifecycle optimization. Measure impact on pipeline, meetings, activation, retention, and CAC payback while partnering with Sales, Lifecycle, Data, and Engineering teams.

Leads architecture, development, testing, and delivery for scalable enterprise software platforms. Drives adoption of AI-assisted development, intelligent automation, AI agents, copilots, observability, anomaly detection, and workflow solutions. Establishes engineering, quality, security, scalability, governance, and responsible AI practices while partnering with engineers, business stakeholders, and senior leadership.

Lead AI Engineer to architect, develop, deploy, and support enterprise-scale agentic AI solutions using LLMs, RAG, model orchestration and secure APIs. Ensures security, governance, and compliance, collaborates cross-functionally, evaluates emerging technologies, contributes to AI roadmap, and mentors junior engineers to drive automation, efficiency, and improved customer experience.

Build and maintain agentic toolkits that let LLMs take actions: design integrations for vendor APIs, write reliable tools, create tests and evals against real API behavior, tune tool descriptions and agent interactions, and contribute shared patterns to scale the catalog.

Embed with clients to discover high-impact AI opportunities, design and build production LLM/RAG/agent systems, deploy secure cloud services, create evaluation and observability tooling, and scale repeatable playbooks while managing client relationships and mentoring junior engineers.

Build and productionize end-to-end AI-powered product features: prototype and iterate on prompts, retrieval flows, evals, backend systems, APIs, data models, and UX to deliver reliable, customer-facing LLM workflows.

Lead architecture, design, and delivery of enterprise-scale Agentic AI solutions. Oversee multiple engineering pods, mentor teams, manage client-facing architecture discussions, ensure production-grade MLOps, cloud infrastructure, microservices, observability, and adoption of emerging AI technologies.

Design, build, and integrate AI applications, automations, and workflows for creative production. Develop APIs, optimize models for performance and cost, create reusable components, troubleshoot production systems, and collaborate with production, creative, and engineering teams to deploy scalable AI solutions.

Own product areas for KIRA’s AI Stockbroking and other financial services. Define user problems, product scope, success metrics, roadmaps, and launch plans. Collaborate with engineering, design, data, operations, compliance, and business teams to simplify complex financial products. Use research, feedback, metrics, experiments, and production insights to launch features, monitor performance, and drive continuous improvement while balancing speed, experience, compliance, and operational complexity.

Own product areas within AI Investing and other financial services, defining user problems, product scope, success metrics, roadmaps, and launch plans. Partner with engineering, design, data, operations, compliance, and business teams to simplify complex financial products, launch features, monitor performance, and iterate using data, feedback, research, and production insights. The role requires strong product judgment, execution, prioritization, communication, and ownership in ambiguous environments.

Own product areas within KIRA’s AI Neobank across financial services such as insurance, payments, savings, investing, and travel. Define user problems, product scope, metrics, roadmaps, and launch plans; collaborate with engineering, design, data, operations, compliance, and business teams; simplify complex financial workflows; and use research, feedback, experiments, and performance data to iterate after launch.

Own AI Finance product areas across insurance, payments, savings, investing, travel, and related financial services. Define user problems, product scope, metrics, roadmaps, and launch plans; collaborate with engineering, design, data, operations, compliance, and business teams. Simplify complex financial products, use research and performance data to guide decisions, launch features, monitor results, and drive continuous iteration while balancing speed, customer experience, business impact, compliance, and operational complexity.

Join Rain's AI team as a Backend Engineer to build scalable backend systems and AI-powered features while collaborating with business teams.

Own reliability, monitoring, and incident response for AI infrastructure; build automation and CI/CD tooling; manage Kubernetes/Docker production workloads; partner with infrastructure, security, and compliance; improve observability and documentation; develop internal full‑stack tooling in Go or Python.

Senior SRE on the IT Operations team owning reliability, monitoring, and incident response for AI infrastructure. Build automation, CI/CD and Kubernetes tooling, improve observability and documentation, and develop internal full-stack tools using Go or Python. Partner with Infrastructure, Security, and Compliance to scale secure, resilient AI deployment pipelines.

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.