Top AI Engineer Jobs

The role involves leading AI product development, working with cross-functional teams to enhance AI systems, and optimizing large-scale production AI systems.

Lead technical projects and design, build, and deploy production-quality AI/ML systems, pipelines, and LLM-based agent platforms. Explore data, develop AI solutions, advise partners on proposals, select appropriate modeling approaches, establish evaluation and observability frameworks, optimize system performance and cost, and guide proprietary AI/ML infrastructure development. Engage directly with clients across industries while promoting best practices, responsible AI governance, and robust engineering standards.

Build and ship LLM- and agent-based prototypes and production solutions. Develop traditional ML where appropriate, prioritize reuse and scalability, collaborate with product, platform, and frontend partners, and support business development with technical credibility.

Design, build, deploy, and scale production-grade AI applications using LLMs and RAG architectures. Develop backend services, application integrations, and scalable cloud infrastructure across AWS, Azure, or GCP. Use Docker and Kubernetes to containerize and orchestrate systems, while optimizing performance, reliability, and monitoring. Collaborate with cross-functional teams on architecture, code reviews, documentation, and engineering practices.

Build and integrate LLM-based agents, coding tools, model infrastructure, and role-specific AI interfaces for engineers, quants, and traders. Develop inference, configuration, integrations, tracing, and access controls; improve system reliability and performance; prototype applications for research, simulation, and trading; and collaborate with users to prioritize solutions.

Design, build, and operate production-grade AI/ML systems and agentic applications end-to-end. Implement data pipelines, model development, RAG and retrieval, observability, performance optimization, and CI/CD deployments in cloud-native environments. Develop multi-agent systems, APIs, and scalable services while ensuring monitoring, latency optimization, and security clearance eligibility.

Set technical direction for AI platform architecture across multiple engineering teams while building production Python systems. Develop agent orchestration, tooling, governance, evaluation, cloud-native services, data persistence, security, testing, CI/CD, and observability patterns. Serve as a technical advisor and embedded expert on complex initiatives, influence roadmaps and staffing, and mentor engineers across the organization.

Build and scale AI agents, tools, infrastructure, and governance systems supporting digital twin workflows for clinical development. Collaborate with research, development, and delivery scientists to create AI-centric solutions, gather user feedback, drive adoption, communicate capabilities, and monitor advances in AI tooling. The role requires healthcare or clinical machine learning experience, AWS expertise, and demonstrated industry execution of AI projects.

Designs, builds, and maintains secure AI-enabled software solutions using machine learning, LLMs, RAG, and agentic workflows. Develops APIs, integrates AI components, deploys applications on cloud platforms, and applies modern software engineering practices. The role requires independent technical leadership, stakeholder communication, mentoring, and collaboration across teams to deliver scalable mission-focused systems.

Designs, builds, and maintains secure AI-enabled software solutions for mission delivery and workflow automation. Responsibilities include developing and deploying LLM, RAG, and agentic applications; building APIs and integrating AI components; using AWS compute, storage, and data pipelines; and applying modern software engineering practices. The role also involves collaborating with stakeholders, explaining AI/ML concepts, working independently, and mentoring team members.

Own shared AI platform components and build production AI features, including LLM orchestration, prompt workflows, evaluation, observability, and deployment. Develop scalable SaaS backend systems, APIs, data pipelines, search, background processing, and PostgreSQL solutions. Lead CI/CD and AI tooling adoption, establish technical direction, mentor engineers, and translate ambiguous requirements into reliable systems.

Architect, build, and deploy production-grade AI/ML platforms and LLM/RAG solutions. Lead system design, CI/CD and Terraform-based IaC, ensure reliability and monitoring, mentor engineers, run incident response, and prototype emerging AI technologies.

Designs and builds governed AI agents, orchestration services, integrations, and enterprise applications for Finance and business systems. The role delivers secure workflows for reporting, procurement, forecasting, billing, and compliance; establishes architecture patterns, deployment pipelines, monitoring, controls, and audit evidence; evaluates AI platforms; and transitions prototypes into reliable production solutions. It also partners with Finance, Security, IT, and executives while enabling teams to adopt safe agent design and compliance-by-design practices.

Build and ship LLM-powered production features end-to-end: prompt orchestration, tool calling, retrieval/RAG, embeddings, evaluation infrastructure, observability, safety guardrails, and integrations. Collaborate with clinical teams, prototype quickly, measure outcomes, and raise engineering standards through mentorship and technical leadership.

Customer-facing engineer who designs, builds, and productionizes GenAI applications (RAG, multi-agent, Text2SQL, fine-tuning). Own production rollouts, advise customers, collaborate with product and engineering, present at conferences, and optimize/evaluate models and deployments across cloud platforms.

Design, develop, and deploy AI-native software products that transform large-scale geospatial data into actionable intelligence. Build multi-agent workflows using orchestration frameworks and large language models, engineer tool-using and self-refining agents, develop data ingestion pipelines, and operate secure, scalable AI systems in production. Apply DevOps, reinforcement feedback mechanisms, and AI-assisted development practices while contributing to engineering standards and documentation.

Leads software engineering initiatives within an agile team, providing technical direction and delivering secure, scalable production code. Establishes governance for AI-assisted development, code review, testing, troubleshooting, and SDLC automation. Shapes product design, technical operations, engineering standards, and reusable frameworks while serving as a subject-matter expert and coaching senior engineers on responsible AI usage, security, resiliency, and validation practices.

Lead embedded AI transformation by partnering with business units to identify high-value opportunities, design and deploy production-ready AI assistants and multi-agent solutions, and coach teams to adopt AI-first practices. Build reusable playbooks, measure business outcomes, and scale capability through hands-on engagements, workshops, and train-the-trainer programs.

Embed with business units to identify and deliver production-ready AI assistants, agents, and orchestration frameworks. Lead discovery-to-deployment engagements, coach technical and non-technical teams, create reusable playbooks and reference architectures, and drive measurable adoption of AI-first workflows across the enterprise.

Lead embedded AI transformation by partnering with business units to identify high-value use cases, design and deploy production-grade AI agents and assistants, and train teams. Build repeatable architectures, playbooks, and training to scale AI adoption, measure business impact, and develop internal champions.

Embed with business units to identify high-value AI opportunities, design and deploy production-ready AI assistants and multi-agent systems, and coach teams to adopt AI-first workflows. Deliver reusable architectures, playbooks, and training while measuring adoption and business impact to scale capability across the organization.

Embed with business units to identify high-value AI use cases, design and deploy production-ready AI assistants and multi-agent solutions, and coach teams to adopt AI-first workflows. Deliver measurable business outcomes, create reusable playbooks and training, and scale organizational capability through hands-on engagements and knowledge capture.

Lead embedded AI transformation by partnering with business units to design, build, and deploy production-grade AI assistants and multi-agent solutions, while training and mobilizing teams through hands-on engagement, playbooks, and scalable adoption frameworks that deliver measurable business outcomes.

Design and build scalable, reliable, observable, and well-tested backend or distributed systems. Use AI-assisted development tools to code, test, document, and design while validating generated outputs. Contribute to architecture and technical direction, apply systems thinking across data models, APIs, and service boundaries, and mentor peers. The role requires collaboration across Product, UX, and Engineering in a hybrid Austin environment.

Designs and develops production-grade AI software for eDiscovery, including predictive and generative AI workflows, document scoring, RAG pipelines, privilege detection, APIs, and scalable Azure services. Integrates model-serving endpoints, vector search, LLM APIs, and platform connectors at multi-million-document scale. The role also requires software architecture, database development, testing, CI/CD automation, agile process improvement, and mentoring engineers.