Top AI Engineer Jobs

Design and build ML DevOps infrastructure and end-to-end pipelines to deploy data, models, and software for Edge AI applications. Implement secure repository and CI/CD/CT practices, collaborate with cross-functional teams, and define metrics and monitoring to ensure delivery and operational performance.

Build and support foundational AI platform components, including LLM inference, model training, similarity search, guardrails, evaluation, governance, experimentation, and observability. Optimize large-scale AI systems for scalability, latency, throughput, hardware utilization, and cost. Contribute to technical strategy and roadmap, collaborate cross-functionally, and lead or mentor engineering teams while applying emerging AI research in production.

Lead AI-powered reporting and insights strategy, design reporting data architecture and pipelines, and implement LLM-powered workflows (RAG, prompt engineering, agentic pipelines). Integrate BI tools and enterprise AI platforms, establish governance for AI outputs, and provide technical leadership to deliver trusted executive insights and scalable self-service reporting.

Design and deploy AI/ML systems: build data pipelines, develop models using Python and TensorFlow, integrate diverse data sources, apply NLP and sentiment analysis, conduct complex data analysis, and collaborate with cross-functional teams to deliver scalable client solutions while continuously learning.

Lead the AI strategy, roadmap, architecture, and development of production-grade AI systems within a strategic business domain. Identify high-value opportunities, drive adoption and measurable business outcomes, establish evaluation, testing, guardrail, and observability practices, and mentor AI engineers and agent builders. Partner with product managers, business leaders, and end users to advance AI transformation while ensuring scalable, reliable, and maintainable solutions.

Build and lead development of an enterprise AI platform supporting large-scale AI-assisted analysis. Responsibilities include designing, developing, testing, deploying, and supporting foundation model training, LLM inference, similarity search, guardrails, evaluation, experimentation, governance, and observability. The role also requires advancing LLM optimization for scalability, latency, throughput, hardware utilization, and cost while shaping the long-term roadmap for foundational AI systems and mentoring engineering teams.

Design, develop, test, deploy, and support foundational AI systems, including model training, LLM inference, similarity search, guardrails, evaluation, governance, experimentation, and observability. Optimize large-scale AI systems for scalability, latency, throughput, hardware utilization, and cost. Partner with engineering, research, product, and program teams; contribute to technical vision and roadmap; and lead or influence teams developing responsible cloud-based AI solutions.

Lead the design, development, deployment, and support of foundational AI systems, including LLM inference, model training, similarity search, guardrails, evaluation, governance, and observability. Optimize production AI systems for scalability, cost, latency, throughput, and hardware utilization. Partner with engineers, scientists, program managers, and product managers while shaping the technical vision and roadmap for responsible AI platforms.

Designs and operates cloud infrastructure and enterprise AI systems, including service integrations, reusable infrastructure-as-code modules, automation frameworks, policy controls, observability, and AI workflow guardrails. Leads incident resolution, architectural improvements, capacity and cost planning, SaaS integrations, and operational process optimization. Creates technical documentation and engineering standards while mentoring engineers and driving adoption of scalable AI and automation practices across teams.

Architects, builds, and operates production-grade agentic AI systems for identity security at enterprise scale. Responsibilities include multi-agent orchestration, tool use, planning, memory, failure recovery, enterprise-grounded reasoning, retrieval and RAG pipelines, model integration and evaluation, safety guardrails, observability, and human-in-the-loop controls. Provides technical leadership through architecture ownership, code reviews, quality standards, and mentoring.

Design, develop, deploy, and support foundational AI platform components, including LLM inference, model training, similarity search, guardrails, evaluation, governance, and observability. Optimize large-scale AI systems for scalability, latency, throughput, hardware utilization, and cost. Contribute to long-term platform strategy, apply advanced AI research in production, and collaborate with engineers, scientists, program managers, and product leaders while mentoring teams.

Design, develop, deploy, and support scalable AI systems, including foundation model training, LLM inference, similarity search, guardrails, evaluation, governance, and observability. Partner with engineering, research, product, and program teams to deliver AI-powered products. Develop state-of-the-art optimization techniques to improve production AI systems’ scalability, cost, latency, throughput, and hardware utilization while contributing to the long-term roadmap for foundational AI platforms.

Own the roadmap and execution for W&B Models, an experiment-tracking platform for AI researchers. Improve experiment organization, training visualization, monitoring, evaluations, and large-scale analysis across web and mobile. Conduct user research, translate ML workflows into product decisions, collaborate with engineering and design, and use data and feedback to launch polished, high-performing experiences. The role requires deep familiarity with ML research, technical product management, data visualization, AI tools, and developer or researcher audiences.

Designs and operates cloud infrastructure and enterprise AI systems, including reusable infrastructure-as-code modules, automation frameworks, SaaS integrations, observability, and policy controls. Leads incident resolution, root-cause analysis, capacity and cost optimization, and operational workflow improvements. Defines secure, scalable AI architectures and guardrails for LLM, RAG, and agentic workflows. Creates architecture documentation, runbooks, self-service resources, and engineering standards while mentoring teams and driving adoption.

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.

Build enterprise AI platform capabilities and production AI applications, including agentic workflows, retrieval-augmented generation, semantic search, vector databases, cloud-native services, and AI model operations. Collaborate with senior engineers on orchestration, developer tooling, governance, security, responsible AI, monitoring, testing, and compliance. Use Python, Google Cloud Platform, Kubernetes, CI/CD, and infrastructure as code while developing scalable AI solutions.

Lead the design and delivery of enterprise AI platform capabilities and production agentic AI solutions. Build multi-agent systems, RAG and knowledge management solutions, cloud-native infrastructure, and MLOps/LLMOps capabilities. Partner with business, architecture, security, legal, and risk stakeholders; influence architecture decisions; mentor engineers; and establish reusable engineering, governance, monitoring, and operational practices.

Designs and delivers enterprise AI platform capabilities and production-ready agentic applications. Builds multi-agent systems, RAG and knowledge platforms, orchestration frameworks, cloud-native services, and developer tooling. Implements MLOps and LLMOps for deployment, evaluation, monitoring, observability, and optimization. Partners with stakeholders, contributes to architecture and engineering standards, and independently owns initiatives from design through production deployment.

Build production AI agents and agent-powered features from research and prototyping through deployment. Develop evaluation infrastructure with golden datasets, regression suites, and offline/online scoring. Own retrieval and context pipelines, production instrumentation, failure taxonomies, and cost and latency budgets. Partner with data science and product management on experiments and agent behavior, while establishing LLM engineering patterns, guardrails, and reusable tooling.

Lead the design, development, deployment, and support of foundational AI systems, including LLM training and inference, similarity search, guardrails, evaluation, governance, and observability. Develop optimization techniques that improve scalability, cost, latency, throughput, and hardware utilization. Partner with engineering, research, product, and program teams while contributing to AI platform strategy, technical vision, and long-term roadmaps.

Lead the design, development, testing, deployment, and support of foundational AI systems, including model training, LLM inference, similarity search, guardrails, evaluation, governance, and observability. Optimize large-scale production AI systems for scalability, latency, throughput, cost, and hardware utilization. Collaborate with engineering, research, program management, and product teams while shaping the technical vision and roadmap for responsible AI platforms.

Build and support full-stack AI solutions using Python, AWS AI services, APIs, integrations, and agentic workflows. Responsibilities include testing, debugging, technical documentation, deployment support, production readiness, and applying information security and data privacy practices, including GDPR and CCPA compliance.

Designs, develops, deploys, and supports enterprise AI applications using AWS AI services, LLMs, generative AI, RAG, AI agents, APIs, and cloud-native architectures. Builds full-stack interfaces and backend services, integrates AI capabilities with enterprise systems, and delivers secure, scalable production solutions. Collaborates with stakeholders, data scientists, architects, product teams, and governance teams while supporting information security, privacy compliance, documentation, and agile development.

Leads end-to-end delivery of production AI, generative AI, and agentic AI solutions. Designs architectures using multi-agent orchestration, RAG, hybrid retrieval, and human-in-the-loop workflows; establishes evaluation, testing, observability, safety, and responsible AI practices. Partners with business and technical teams to measure value, mentors engineers and data scientists, defines engineering standards, and influences enterprise AI strategy and roadmaps.

Build and scale AI-ready data platforms for biotherapeutics research. Develop data products, machine learning and agentic AI pipelines, integrate public and proprietary datasets, and improve data governance, metadata, storage, visualization, and analysis. Collaborate with scientists, external companies, and academic institutions while supporting platform adoption through training and workshops. Publish and present scientific work and take solutions from prototype to production.