Top AI Engineer Jobs

Lead architecture and technical direction for Scale's Generative AI Data Engine, embedding into high-priority Contributor teams to unblock delivery, overhaul platform infrastructure for LLM-native workloads, ensure scalability/reliability, mentor engineers, partner with product/leadership, and own large end-to-end backend, frontend, and ML system initiatives.

Act as primary technical partner for enterprise customers, troubleshoot complex issues across contact center platforms and integrations, coordinate escalations, leverage AI tools for insights, drive customer outcomes, share feedback with Product/AI teams, mentor peers, and shape AI-augmented support processes.

Embed with cross-functional teams to design, build, and maintain AI applications. Translate business requirements into production software, implement and integrate AI/LLM features, and collaborate closely with stakeholders on product outcomes.

Own end-to-end dealer implementations for an AI-driven SaaS product: configure integrations, validate data flows, train non-technical staff, manage go-live timelines and stakeholders, troubleshoot live issues, write runbooks/SOPs, and route field feedback to product and engineering.

Lead Azure-based application development and DevOps initiatives, building internal .NET and Python applications, APIs, CI/CD pipelines, and IAM solutions. Evaluate and pilot Azure AI and Copilot integrations, design agent-based workflows, support operational reliability, and participate in a 24x7 on-call rotation while collaborating with cross-functional teams.

Design, deploy, and operate infrastructure and tooling for ML and LLM workloads. Build containerized AI services, CI/CD pipelines, and IAC using Terraform. Manage cloud and on-prem environments, ensure reliability, observability, security, and scalability of production AI systems. Integrate AI tooling with data and internal systems, prototype new technologies, troubleshoot issues, and document runbooks.

Design, prototype, and operationalize AI/ML solutions (including LLMs and RAG) for a USG program. Curate data pipelines from enterprise financial and scheduling datasets, lead LLM training/fine-tuning, scout commercial AI technologies, draft SOWs, evaluate contractor deliverables, and advise on secure deployment and model lifecycle management in high-security environments.

Build and operate core AI agent infrastructure: orchestration, context management, filesystem and sandboxed execution, model routing and serving, databases, Kubernetes, networking, and cloud systems to make long-running agent workloads fast, reliable, safe, and cost-efficient.

Build, test, deploy, and monitor production-grade generative AI applications and APIs. Collaborate with data scientists, engineers, and stakeholders to implement RAG, prompt orchestration, vector DBs, and evaluation pipelines on AWS. Write maintainable, well-tested code and contribute to documentation, code reviews, observability, and operational best practices.

Partner with customers to architect, build, and deploy high-scale production AI applications. Translate ambiguous goals into reliable, observable services, implement end-to-end model deployment and monitoring, optimize ML projects, and own customer-facing technical delivery and product outcomes.

Design, develop, test, and maintain mission-critical Windows desktop (WPF/MVVM), Android (Kotlin/Java), and distributed microservice applications. Work on agile teams, perform peer reviews, CI/CD, research and prototype R&D projects, and learn emerging technologies to support DoD and federal clients.

Design and build agentic AI systems for cybersecurity: architect AI cyber taskforce and production-grade agentic components, implement ranking/retrieval/search powered by LLMs, design training/evaluation frameworks, decompose security workflows, address scale/performance, and mentor engineers while contributing to engineering and operational excellence.

Design and implement scalable data solutions using Microsoft Fabric and Azure. Build and orchestrate ETL/ELT pipelines, develop Power BI reports and DAX, optimize T-SQL and KQL queries, model data for Lakehouse/warehouse architectures, integrate varied data sources with Spark/PySpark, automate processes, ensure governance and security, and troubleshoot pipeline issues.

Partner with functional SMEs to map and redesign processes using AI-first solutions. Prototype and build agentic workflows and integrations (Box, Workday, Salesforce, NetSuite, BigQuery), own data pipelines, apply security/compliance controls, operate production systems, and evangelize AI automation across teams.

Build and ship full‑stack, LLM-powered features for an edtech product: tutoring agents, parent/coach copilots, retrieval over student data, and AI evals. Own small systems end-to-end, work closely with founders and senior engineers, ship weekly, and write evals and regression tests for AI features.

Design, build, and operate AWS-based production platform; improve CI/CD, reliability, observability, incident response, cost efficiency, and guardrails for agent-involved workflows. Drive operational analytics, testing/rollback mechanisms, human-in-loop approvals, and compliance foundations.

Build and ship agent-driven internal products and Slack interfaces, extend and steward the Centaur runtime and agent CLIs, integrate harnesses and developer workflows, and maintain open-source community-facing docs, triage, and PRs to improve firm operations and developer extensibility.

Build and operate the core control plane and durable services coordinating agent runs; develop and harden a sandboxed Kubernetes runtime; manage secrets and secure egress; own reliability, monitoring, incident response, and failure recovery; and maintain self-hosted deployment, configuration, and upgrades for Centaur.

Lead design, development, testing, and App Store submission of production-scale iOS features in Swift. Collaborate cross-functionally, enforce code quality via reviews and refactoring, maintain technical documentation, and adopt AI-assisted tooling to improve productivity across large, user-facing modules.

Pioneering machine learning solutions for the defense sector, developing and deploying production-grade ML models, tackling complex data challenges, and collaborating with engineers.

Lead technical evaluations and pre-sales activities for data-driven, AI-powered Salesforce solutions. Design data architectures and AI use cases, deliver demos, proofs of concept, and workshops, advise customers on data ingestion/integration and real-time analytics, and collaborate with sales and marketing to drive revenue.

Design and build end-to-end full stack solutions that integrate AI/ML models and pipelines. Develop frontends, backends, APIs, and dashboards; deploy scalable, secure cloud and containerized systems; create MCP tools; collaborate cross-functionally and produce technical documentation and proofs of concept.

As a Senior Applied AI Engineer at Vanta, you'll design and implement AI-powered features, mentor engineers, and ensure AI systems improve customer experience, leveraging LLMs and full-stack development skills.

The Senior Software Engineer leads backend system development, mentors team members, and collaborates cross-functionally to deliver high-quality APIs and applications.

As a Software Engineer on Applied AI, you will build and operate scalable systems for data generation and model evaluation, collaborating closely with AI researchers and customers to deliver high-impact solutions.