Top AI Engineer Jobs

Design, build, test, deploy, and maintain cloud-based web applications and microservices. Ensure performance, reliability, and code quality while collaborating on new features, debugging, and optimizing systems. Use AI development tools daily, contribute to infrastructure as code, and promote maintainable architecture and documentation.

Own Greenboard’s quality strategy as its first dedicated Automation Engineer. Build the test framework, automated regression suites, QA practices, release gates, and critical-path validation for compliance workflows. Develop methods for evaluating nondeterministic AI outputs using golden datasets and quality thresholds. Validate data integrity across large-scale ingestion pipelines, conduct bug triage, support release sign-off, and collaborate closely with engineers and regulated financial-services customers.

Designs and implements AI/ML capabilities for cyber and intelligence workflows, including LLM-powered and agentic automation. Captures mission-user workflows, integrates AI systems with APIs and enterprise data stores, and delivers reliable solutions for classified environments. Collaborates with analysts, operators, developers, and leadership to improve analytical efficiency, data accessibility, and decision-making while meeting security and governance requirements.

Build production-grade AI features and LLM pipelines (RAG, prompt engineering, context management) for financial-data workflows. Implement agentic workflows with Agno, optimize for accuracy/latency/cost, evaluate output quality, and integrate AI capabilities with backend APIs while maintaining code quality, observability, and testability.

Lead customer-facing deployments of agentic AI for chip design: onboard and integrate ChipAgents, prototype full-stack solutions (backend models to frontend), collaborate on model and infrastructure improvements, create docs and dashboards, and remain hands-on with code, prompt engineering, and workflow orchestration to drive adoption and iterate from field feedback.

Build high-performance API services and production-grade Generative AI systems. Design and implement end-to-end RAG pipelines, vector stores, embeddings, and latency-optimized generators. Develop agentic AI using LangChain/CrewAI/Semantic Kernel, integrate LLM APIs and AWS Bedrock, manage CI/CD and ML pipelines, and apply evaluation frameworks to detect hallucinations and optimize performance.

Lead design, development, testing, and deployment of production-grade agentic AI systems. Architect integrations with enterprise systems, drive advanced analytics and experimentation, mentor engineers, and own model lifecycle, reliability, and innovation for agentic/autonomous solutions.

Design and operate Procore’s next-generation cloud compute and data platform in a FedRAMP-compliant environment. Build and support distributed, highly available systems; manage Kubernetes clusters, cloud-native infrastructure, and infrastructure as code; participate across the software development lifecycle; run technical proofs of concept; and collaborate with product, UX, IT, and engineering teams to shape platform capabilities and roadmap.

Build and deploy AI-powered tools and workflows across performance marketing, creative production, CRM, landing pages, growth operations, and product growth. Automate analysis, personalization, content generation, campaign activation, reporting, and conversion optimization. Integrate LLM APIs, marketing platforms, data pipelines, and orchestration tools while mentoring growth teams on AI adoption and partnering cross-functionally to deliver production-grade solutions.

Provides operational support and issue resolution for AI infrastructure and platform services. Manages Kubernetes and Docker deployments, monitors distributed workloads, and develops Python and Shell automation. Uses GitLab CI/CD, JIRA, ServiceNow, and Azure AD while troubleshooting networking and storage issues. Documents infrastructure processes and runbooks, collaborates cross-functionally, and participates in periodic on-call rotations, including occasional weekend coverage.

Own presales discovery, solution architecture, technical demos, prototypes, proof-of-concept definition, technical evaluations, RFP responses, and security questionnaires for enterprise AI customers. Translate customer workflows and technical requirements into validated architectures and measurable business value, manage implementation risks, and provide accurate delivery handoffs. Convert field insights into reusable reference architectures, demos, and product improvements.

Build and maintain scalable data platforms and pipelines that integrate structured and unstructured data. Retrieve, merge, and analyze data from APIs and databases using Python, R, and SQL. Prepare datasets for AI and generative AI models, evaluate machine learning performance, and guide multidisciplinary teams in agile client projects supporting fraud detection, cancer research, and national intelligence missions.

Owns the secure platform foundation for AI systems across cloud, hybrid, on-premises, and air-gapped environments. Responsibilities include platform architecture, GitHub Enterprise administration, Terraform automation, identity and secrets integration, software supply-chain security, backup and disaster recovery, observability, vulnerability remediation, threat detection, and incident response. The role also leads Zero Trust segmentation and operates reliable infrastructure supporting critical engineering platforms.

Build and maintain backend services, APIs, and integration layers for a modular ecommerce platform. Integrate personalization, semantic and LLM search, DAM, PIM, SAP/HANA, ERP, analytics, and customer data systems. Use AI coding agents throughout development while validating code quality, security, performance, and reliability. Design scalable APIs, automated testing, observability, and reusable architecture while collaborating with internal teams, vendors, and implementation partners.

Own end-to-end AI product development for enterprise clients, from requirements and rapid LLM prototyping through production deployment. Responsibilities include data modeling, ETL pipelines, integrations, evaluation, observability, security, reliability, cost management, and client communication. The role requires shipping real AI products, iterating based on usage, and independently managing ambiguous technical and product decisions.

Build and improve an AI intelligence platform that transforms structured and unstructured data into business intelligence. Develop scalable data pipelines, dashboards, reporting systems, analytical models, AI workflows, prompt strategies, evaluation frameworks, and automation. Analyze restaurant and social media data to identify trends and opportunities, while monitoring quality, troubleshooting issues, and improving reliability and scalability. Establish best practices for AI-assisted analysis and mentor team members.

Designs and integrates AI-enabled modernization solutions while establishing quality assurance frameworks for AI-generated code, skills, agents, and prototypes. Develops test plans, acceptance criteria, regression suites, automated testing, security scans, and quality gates. Collaborates with DevOps teams on reliable multi-agent workflows, validates prototypes for production readiness, and monitors accuracy, security, compliance, and data-sensitivity risks.

Leads enterprise software architecture, technical strategy, and roadmap execution across multiple domains. Designs scalable, reliable systems and platforms using modern programming languages, cloud services, microservices, databases, containers, and DevOps practices. Partners with product and engineering teams, mentors technical staff, influences executives, improves engineering processes, evaluates performance and costs, and drives continuous learning. The role requires extensive software development and architecture experience, strong cloud and Azure expertise, and the ability to transfer research into production.

Develop and sustain cloud software and AI/ML capabilities for national security applications. Responsibilities include requirements analysis, Java-based distributed software development, AWS cloud integration, RESTful services, testing, deployment, troubleshooting, and technical documentation. The role works with Hadoop and big-data technologies, Linux, Kubernetes, infrastructure as code, CI/CD, LLMs, RAG, and agentic AI. An active TS/SCI clearance with polygraph and at least five years of software engineering experience are required.

Performs security assessments and engineering for AI/ML systems, large-scale compute clusters, AWS Cloud infrastructure, networks, and enclave environments. Designs and integrates information assurance architectures, identifies and mitigates security risks, validates security requirements, supports certification and accreditation, and enforces trusted relationships and government security policies. Collaborates with engineering teams and government customers on secure system development and operational deployment.

Develop and sustain cloud software supporting cyber intelligence and AI/ML capabilities on AWS and large-scale compute clusters. Responsibilities include requirements analysis, Java software development, cloud integration, testing, troubleshooting, RESTful services, distributed systems, big-data technologies, Linux development, and technical documentation. The role also involves LLM, RAG, agentic AI, Kubernetes, infrastructure-as-code, CI/CD, and cloud security practices. An active TS/SCI clearance with polygraph is required.

Develop and sustain cloud software supporting cyber intelligence and AI/ML capabilities on large-scale compute clusters and AWS infrastructure. Responsibilities include requirements analysis, software development, integration, testing, validation, deployment, maintenance, monitoring, and issue resolution. The role uses distributed NoSQL technologies, big-data frameworks, cloud services, AI/ML algorithms, LLMs, RAG, and agentic workflows to support national security missions.

Lead development of an AI assistant and MCP server for a DoD RF spectrum monitoring application. Design tool interfaces, deploy local models in air-gapped environments, integrate LLM functionality across FastAPI, React, and PostgreSQL systems, and develop prompts, evaluation frameworks, and context strategies. Collaborate with engineering and domain stakeholders to identify use cases, improve assistant reliability, document architecture, and support Python and TypeScript/Node.js services.

Designs AI/ML data-fusion and sense-making solutions for intelligence and space missions. Develops architectures, testing and validation frameworks, responsible AI practices, adoption roadmaps, and mission applications across diverse data modalities. Collaborates with business development and program teams, mentors technical staff, and communicates AI capabilities and limitations to leadership and customers. Requires defense or intelligence experience, Secret clearance with ability to obtain TS/SCI, and full-time onsite work.

Develops and leads systems engineering activities for cyber intelligence and AI/ML capabilities hosted on large-scale compute clusters and AWS Cloud. Responsibilities include requirements analysis, system architecture and design, interface definition, technical documentation, risk and configuration management, systems integration and testing, verification planning, and security and accreditation support. The role provides technical leadership across integrated product teams and coordinates with government stakeholders to ensure system technical integrity.