Top AI Engineer Jobs

Design, develop, deploy, and optimize AI and machine learning solutions for an employee leave management platform. Responsibilities include building scalable models, applying NLP and predictive modeling, implementing MLOps practices, developing secure data pipelines, collaborating with product and engineering teams, and ensuring ethical AI through fairness, transparency, and bias detection.

Leads the design and development of scalable software frameworks and secure production code. Architects enterprise AI-enabled engineering workflows, establishes responsible AI guardrails, and improves automation, testing, release readiness, and incident response. Advises cross-functional teams, influences senior stakeholders, defines target-state AI architectures, and guides roadmaps and solution architectures across Consumer and Community Banking.

Design, develop, deploy, and support large-scale AI systems, including foundation model training, LLM inference, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. Establish enterprise AI architecture standards, optimize model performance and infrastructure costs, lead hybrid and multi-model orchestration, promote responsible AI, and mentor engineering teams while influencing technical strategy and research-to-production processes.

Design, develop, optimize, deploy, and support foundational AI systems, including LLMs, agents, vector search, model evaluation, orchestration pipelines, and responsible AI controls. Lead cost-performance governance, technical design reviews, roadmap development, and optimization of model and hardware utilization. Mentor senior AI engineers while partnering with research, product, program, and engineering teams to deliver scalable production AI platforms.

Design, develop, deploy, and support scalable AI systems, including foundation models, LLM inference, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. Establish enterprise AI architecture standards, optimize model performance and infrastructure costs, guide hybrid and multi-cloud strategies, and promote responsible AI practices. Lead technical direction, mentor engineering teams, collaborate with cross-functional stakeholders, and advance research techniques into production.

Train, fine-tune, and deploy generative AI models for realtime interactive avatars. Build multimodal video and audio data pipelines, optimize low-latency inference, maintain distributed GPU training infrastructure, define quality metrics, and collaborate across research, backend, and product teams to ship avatar features.

As an AI Engineer at Pereview Software, you will design and enhance AI applications, manage data extraction workflows, and integrate AI technologies to improve business operations in commercial real estate.

Build and deploy production-grade agentic AI and generative AI solutions using Python, Claude Agent SDK, and AWS Bedrock AgentCore. Develop agent orchestration, RAG pipelines, document-processing workflows, CI/CD automation, and enterprise integrations. Deploy and maintain Langfuse for observability, tracing, monitoring, and continuous evaluation. Implement automated testing, LLM-as-a-Judge evaluation, performance benchmarks, security controls, and production troubleshooting across scalable AWS architectures.

Lead enterprise deployments of Talkdesk’s Agentic AI platform by architecting, building, testing, and deploying production AI agent solutions. Own technical execution, integrations, security, scalability, risk mitigation, and customer outcomes. Advise customer technical leaders, influence product and engineering roadmaps, develop reusable tooling, and mentor junior engineers. The role requires strong software engineering, AI architecture, systems integration, prompt engineering, and client-facing project leadership experience.

Serves as the senior technical authority for enterprise browser data protection and security. Owns architecture, roadmap, standards, lifecycle, browser-based DLP controls, integrations, automation, and operational support. Leads complex troubleshooting, major incident response, root-cause analysis, runbook development, and continuous improvement. Partners across cybersecurity, identity, endpoint, network, cloud, risk, compliance, and vendor teams while mentoring engineers and influencing enterprise security design.

Develop and deploy agentic AI and conversational voice solutions to modernize enterprise IVR and contact center applications. Responsibilities include integrating LLMs, NLP, speech technologies, APIs, microservices, RAG, orchestration workflows, and cloud platforms; implementing security and privacy controls; testing and optimizing performance; and collaborating with technical and customer stakeholders in Agile environments.

Implement AI strategies and integrate generative AI capabilities, including prompt orchestration, tool calling, embeddings, and fine-tuning, into applications. Build and operate cloud-native and on-premises AI infrastructure, develop RAG and LLM solutions, deliver POCs and scalable production systems, optimize token efficiency and costs, and ensure responsible AI, security, and compliance across client environments.

Build and maintain production AI tools for marketing, including content generation, meeting preparation, SDR workflows, case study automation, and integrations with marketing platforms. Develop and evaluate prompts, monitor quality, troubleshoot live tools, document solutions, train marketing users, and support AI project intake. The role requires API integration, automation, basic scripting, strong communication, and comfort working in an evolving environment.

Leads technical direction for Uber’s AI developer platform, building agentic systems that plan, code, review, verify, and ship software. Responsibilities include architecting durable workflows, setting AI evaluation and reliability standards, partnering across engineering organizations, shipping critical platform components, mentoring technical leaders, and representing the platform externally. The role requires large-scale backend or developer-platform engineering experience, strong programming skills, and technical leadership across multi-team initiatives.

Develop and deploy production AI agents using the Claude Agent SDK and Amazon Bedrock AgentCore. Build agent workflows with Python or TypeScript, tool calling, MCP integrations, and API orchestration. Create GitHub Actions CI/CD pipelines, configure AWS IAM, ECR, Docker, authentication, logging, monitoring, and observability. Troubleshoot and optimize production performance. The role is remote or hybrid for candidates in western U.S. time zones and requires client-site travel.

Lead enterprise Oracle EBS R12 to Oracle Fusion Cloud ERP migration programs from discovery through post-go-live stabilization. Manage scope, schedule, budget, risks, dependencies, resources, integrations, data migration, testing, cutover, vendors, and globally distributed teams. Coordinate functional and technical stakeholders, oversee implementation partners, provide executive reporting, and support process redesign, adoption, training, and organizational readiness.

Architect and scale Oura’s agentic software-development pipeline across the full SDLC. Build reliable AI workflows for planning, coding, testing, review, CI repair, release verification, and incident follow-up. Establish evaluation, tracing, metrics, sandboxing, permissions, and privacy safeguards for production agents. Lead adoption across engineering squads, set technical direction, and partner with platform, security, privacy, infrastructure, mobile, backend, product, and design teams.

Build and operate software and AI tools supporting research into fault-tolerant neutral atom quantum computing architectures. Collaborate closely with a tight-knit team to design and develop systems for the company’s quantum computer. Prior quantum computing experience is not required, and candidates at all experience levels are welcome.

Provides Level 3 customer support for HPE Cray Programming Environment software. Analyzes, reproduces, isolates, debugs, and resolves complex issues; develops workarounds; escalates unresolved problems to HPE R&D or external partners; performs pre-release testing; documents issue resolution; supports customers after hours; and mentors less-experienced staff.

Leads aerospace system and component testing, including test planning, procedures, reports, requirements management, systems integration, and Verification and Validation activities. Troubleshoots complex issues using schematics, service manuals, testing equipment, and logical analysis. Manages test-related nonconformances, documents technical findings, supports multidisciplinary teams, and guides less experienced engineers while ensuring compliance with system requirements and quality standards.

Build and ship end-to-end AI applications across frontend, backend, APIs, data pipelines, retrieval systems, and LLM integrations. Develop reference applications and reusable components, experiment with RAG and agentic architectures, and improve AI application quality, latency, reliability, and cost. Collaborate with customers, partners, product, and engineering teams while translating real-world problems into production software and sharing technical best practices.

Design, build, and deploy production AI applications, agentic systems, retrieval pipelines, MCP integrations, and modernized enterprise platforms. Architect guardrails, memory, context management, security controls, identity, and human oversight. Work directly with clients and multidisciplinary teams to define ambiguous problems, shape delivery scope, and implement scalable solutions across discovery through deployment. Contribute reusable AI engineering patterns and tooling for future engagements.

Design, develop, deploy, and optimize scalable AI/ML solutions, including LLM applications, RAG pipelines, AI agents, and MLOps workflows. Build production-grade ML pipelines, automation frameworks, monitoring, evaluation, and governance capabilities using Python and AWS services. Collaborate with technical and business teams to integrate reliable AI solutions into production systems and continuously improve model performance, scalability, and compliance.

Owns the reference architecture, engineering standards, and technical direction for enterprise AI agents and machine learning systems across cloud and on-premises environments. Designs and implements complex AI platforms, self-hosted LLM solutions, agent architectures, safety guardrails, model governance, and GPU infrastructure. Provides technical mentorship, evaluates emerging tools, partners with data engineering, and serves as the escalation point for complex engineering challenges.

Designs, develops, and deploys scalable AI-powered full-stack applications. Responsibilities include building React or Angular front ends, backend services, APIs, microservices, AI/ML models, LLM and RAG integrations, automation solutions, data pipelines, and cloud-native deployments using AWS, Docker, and Kubernetes. The role also involves database integration, CI/CD, testing, secure coding, rapid prototyping, and collaboration across teams.