Top AI Engineer Jobs

Architect and lead enterprise AI infrastructure supporting LLM training and inference, machine learning, agentic AI, and production platforms. Define technical standards for HPC, GPUs, Kubernetes, private and hybrid cloud, networking, storage, reliability, security, observability, and automation. Solve complex scalability and performance challenges, guide cross-functional initiatives, evaluate emerging technologies, mentor engineers, and embed governance, compliance, privacy, and responsible AI into secure, resilient infrastructure.

Architect and engineer secure, scalable AI infrastructure supporting LLM training and inference, machine learning, agentic AI, and production platforms. Lead HPC, GPU, Kubernetes, cloud, networking, storage, reliability, observability, security, and governance initiatives. Shape enterprise architecture and engineering standards, solve complex performance and scalability challenges, evaluate emerging technologies, lead cross-functional technical programs, and mentor engineers.

Architect and lead enterprise AI infrastructure supporting LLM training and inference, machine learning, agentic AI, and emerging production capabilities. Define technical standards across HPC, GPUs, Kubernetes, hybrid cloud, networking, storage, reliability, observability, security, and automation. Solve complex scalability and performance challenges, embed governance and compliance, evaluate emerging technologies, lead cross-functional initiatives, and mentor engineering teams.

Architect and lead enterprise AI infrastructure supporting LLM training and inference, machine learning, agentic AI, and production platforms. Responsibilities include HPC and GPU architecture, Kubernetes, private and hybrid cloud, high-performance networking, storage, observability, security, reliability, governance, and automation. The role drives technical standards, performance engineering, cross-functional initiatives, emerging technology evaluation, and engineer mentorship in a regulated environment.

Architect and lead enterprise AI infrastructure supporting LLM training and inference, machine learning, agentic AI, and production platforms. The role covers HPC, GPUs, rack-scale systems, private and hybrid cloud, Kubernetes, networking, storage, observability, security, reliability, governance, and automation. Responsibilities include solving complex scalability and performance challenges, defining engineering standards, evaluating emerging technologies, leading cross-functional initiatives, and mentoring engineers in a regulated environment.

Design, build, and operate secure LLM platforms and RAG pipelines for security analytics and automation. Develop agentic workflows, continuous monitoring, governance, and adversarial testing to mitigate AI risks and integrate protections into security tooling and cloud-native ML pipelines.

Design, develop, deploy, and support scalable AI systems, including foundation model training, LLM inference, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. Optimize AI infrastructure for cost, latency, throughput, reliability, and hardware utilization. Define system architectures and SLOs, lead technical reviews, ensure security and compliance, contribute to long-term AI platform strategy, and mentor engineering colleagues.

Leads development of secure, scalable, cloud-native software platforms and distributed services. Designs systems, writes and reviews production code, supports cloud migrations, and improves reliability through testing, operations, observability, and infrastructure as code. Drives adoption and governance of AI-assisted engineering practices, validates AI outputs for security and correctness, mentors engineers, and influences architecture and delivery practices across teams.

Leads the architecture and evolution of a back-end CAD framework for ASIC physical implementation. Develops advanced-node synthesis, place-and-route, timing closure, power optimization, signoff, automation, and AI-assisted design methodologies. Partners with physical design teams and EDA vendors, evaluates Synopsys and Cadence tools, improves power, performance, area, runtime, and productivity, and mentors engineers.

Designs, develops, deploys, and optimizes production AI systems, including foundation models, LLM inference, agentic workflows, vector search, guardrails, evaluation, governance, and observability. The role establishes AI cost-performance standards, leads technical design reviews, contributes to platform roadmaps, optimizes model and hardware utilization, and mentors senior AI engineers while delivering scalable and responsible AI capabilities.

Develop and operate large-scale generative AI systems, including foundation models, LLM inference, agentic workflows, guardrails, model evaluation, vector search, memory, governance, and observability. Lead architecture and optimization for cost, latency, throughput, reliability, and hardware utilization. Define AI service objectives, ensure security and compliance, guide technical roadmaps, and mentor engineering teams.

Designs, develops, deploys, and supports production AI systems, including foundation models, LLM inference, agentic workflows, guardrails, evaluation, and observability. Owns complex AI architecture, reliability objectives, governance, and optimization for cost, latency, throughput, and hardware utilization. Partners across engineering, research, product, and program teams; leads technical reviews; contributes to long-term platform strategy; and mentors senior technical associates.

Designs, develops, deploys, and supports foundational AI systems, including LLM inference, agentic workflows, vector search, guardrails, evaluation, and multi-model orchestration. Optimizes AI systems for cost, latency, throughput, scalability, and hardware utilization. Leads governance reviews and design councils, contributes to technical roadmaps, establishes responsible AI standards, and mentors senior engineering leaders.

Build and operate scalable generative AI systems, including foundation model training, LLM inference, agentic workflows, vector search, guardrails, evaluations, governance, and observability. Optimize AI systems for cost, latency, throughput, and hardware utilization; architect multi-model pipelines; establish performance governance and engineering standards; contribute to long-term platform strategy; and mentor senior AI engineers.

Design, develop, deploy, and support large-scale AI systems, including foundation models, LLM inference, agentic workflows, vector search, guardrails, evaluation, governance, and observability. Optimize model performance, scalability, latency, throughput, hardware utilization, and cost. Lead technical reviews and governance, define AI platform roadmaps, integrate heterogeneous AI systems, and mentor senior engineering talent.

Design, develop, deploy, and support large-scale generative AI systems, including foundation models, LLM inference, agentic workflows, vector search, guardrails, evaluation, and observability. Optimize AI systems for cost, latency, throughput, scalability, and hardware utilization. Establish governance and engineering standards, lead design reviews, contribute to platform strategy, and mentor senior AI engineers.

Design, develop, and deploy scalable Voice AI platforms and distributed systems across regions and datacenters. Build audio acquisition, conversation management, speech generation, model integrations, and voice harness capabilities. Partner with product managers and customers, improve SRE tooling, enforce software engineering best practices, and mentor engineering colleagues. The role requires extensive experience with LLM applications, AI agents, programming, Kubernetes, and highly available workloads.

Design, develop, and operate scalable Voice AI platforms, including audio acquisition, conversation management, speech generation, model integrations, and voice harnesses. Build distributed systems across regions and datacenters, develop LLM-based applications and agents, improve SRE tooling, enforce software engineering practices, collaborate with customers and product managers, and mentor engineering colleagues.

Develop and support enterprise AI applications under senior engineering guidance. Responsibilities include building AI components with Python or C#/.NET, retrieval-augmented workflows, conversational Microsoft Teams experiences, REST integrations, deployments, infrastructure automation, model and prompt validation, observability, documentation, and Agile collaboration. The role emphasizes responsible AI, security, privacy, human-in-the-loop design, and learning established engineering practices.

Hands-on technical leader who designs, builds, and deploys production-grade AI automation and agentic workflows. Responsibilities include rapid prototyping, RAG and document-intelligence systems, API integrations, orchestration, monitoring, reusable AI assets, and mentoring a small engineering team to deliver frequent releases and measurable business impact.

Supports AI engineering initiatives at Vantor, contributing to spatial intelligence solutions and helping develop technologies that enable customers to understand current and future conditions. The posting provides limited role-specific responsibilities but emphasizes problem-solving, innovation, collaboration, and mission-focused work. Applicants must qualify as a U.S. Person under applicable export control and ITAR requirements.

Build and operate highly available, cloud-native Voice AI systems, including deployments, observability, monitoring, disaster recovery, and continuous integration. Develop scalable software, integrate LLMs with voice and real-time communication platforms, automate testing and delivery, and collaborate with product owners. Mentor colleagues and promote knowledge sharing across telecommunications and AI engineering disciplines.

Designs and maintains scalable backend services, APIs, event-driven microservices, databases, and reliable data pipelines using Java and Python. Builds AI/ML-powered features, including inference services, prompt orchestration, vector search, and ML pipelines. Ensures production readiness through CI/CD, automated testing, observability, monitoring, incident response, and secure coding. Collaborates with product, QA, SRE, data science, and stakeholders while mentoring engineers and leading technical design discussions.

Leads the strategy, design, and deployment of AI and machine learning solutions for tax-focused client engagements. Builds Python-based systems for text analytics, extraction, and sentiment analysis; oversees data preparation, feature engineering, model tuning, and platform integration. The Director develops client relationships, drives business development, mentors teams, promotes responsible AI adoption, and ensures governance and professional standards across initiatives.

Lead the architecture, development, and production deployment of advanced AI systems for risk management and compliance. Design agentic frameworks, LangGraph workflows, distributed memory infrastructure, model-serving platforms, PEFT optimization, and AI observability solutions. Mentor engineers, establish technical strategy, collaborate across teams, and evaluate emerging GenAI, LLM, and NLP technologies. The role also requires integrating AI APIs and microservices with banking applications while ensuring scalability, reliability, compliance, and operational excellence.