Top AI Engineer Jobs

Leads AI engineering initiatives for tax technology, designing and deploying machine learning solutions using Python, deep learning, and NLP. Builds data pipelines, validates models, guides experimentation, and translates tax business needs into scalable production systems. Oversees complex workstreams, directs cross-functional teams, presents technical recommendations, coaches team members, and ensures solutions meet technical, client-service, policy, and responsible AI standards.

Design, customize, test, and troubleshoot Guidewire applications supporting insurance policy, claims, and billing workflows. Develop AI-enabled solutions, integrations, and automation; translate client requirements into technical solutions; interpret system data; and contribute to digital transformation engagements. The role also involves managing workstreams, reviewing junior team members’ work, guiding technical problem solving, and building client relationships while supporting Guidewire platform modernization.

Designs and delivers trustworthy AI, machine-learning, data, and software solutions from problem definition through production operation. Responsibilities include predictive and generative AI development, data and feature engineering, application and API delivery, evaluation, monitoring, responsible AI governance, and technical leadership. The role requires production-quality Python and SQL, large-scale data platform experience, cloud and AI tooling, and the ability to guide cross-functional teams in deploying secure, maintainable AI capabilities.

Develop and support AI-enabled applications and automation using Python, .NET, LLMs, machine learning, APIs, and cloud services. Responsibilities include building AI workflows and RAG solutions, integrating enterprise systems, evaluating model performance, troubleshooting applications, and following security, privacy, governance, and responsible AI practices. The role also involves prototyping, testing, documentation, collaboration with business and technical teams, and continuous learning.

Design and deploy AI systems and machine learning models, perform data wrangling and analysis, and integrate solutions into data infrastructure. Develop scalable AI applications, relational data systems, evaluation tools, automated tests, and monitoring capabilities. Assess AI behavior, robustness, bias, explainability, drift, hallucinations, and guardrails while evaluating technical controls and communicating findings to clients and stakeholders.

Design, develop, deploy, and support foundational AI systems, including model training, LLM inference, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. Optimize large-scale AI systems for cost, latency, throughput, scalability, and hardware utilization. Own complex architectures, define reliability objectives, lead technical reviews, ensure security and compliance, shape long-term AI roadmaps, and mentor engineering staff.

Leads the design, development, and integration of complex generative AI solutions, including agentic and multi-agent systems deployed on cloud and on-premises platforms. Provides technical leadership, manages AI project lifecycles, collaborates with product and business stakeholders, and mentors AI engineers. Requires expertise in Python, TensorFlow, PyTorch, Azure AI, AWS Bedrock, and machine learning, with preferred experience in LLMOps and Kubernetes.

Design, develop, optimize, deploy, and support foundational AI systems, including LLM inference, agentic workflows, vector search, model evaluation, orchestration, governance, and observability. Lead cost-performance optimization across production AI platforms, establish engineering standards, contribute to technical roadmaps, lead design reviews, and mentor senior AI engineers. Collaborate with research, engineering, program management, and product teams to deliver scalable, responsible AI products.

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, and hardware utilization. Own complex system architecture, define reliability objectives, lead technical reviews, ensure security and compliance, contribute to long-term AI platform strategy, and mentor engineering colleagues.

Build developer tooling, reusable frameworks, AI agent workflows, MCP integrations, and cloud-native services that improve engineering productivity. Evaluate and roll out AI development technologies, establish standards and guardrails, support authentication platform evolution, and improve AWS service and job creation paths. The role also involves documentation, collaboration with engineering and product teams, and improving system reliability and performance.

Software Engineering Intern developing full-stack applications, AI agents, intelligent automation, MCP-enabled applications, monitoring tools, and enterprise platforms. Responsibilities include designing scalable software, integrating APIs and databases, creating analytics and system health capabilities, collaborating cross-functionally, and supporting secure, reliable continuous improvement. The role offers hands-on experience with modern programming languages, cloud-native technologies, AI-enabled development workflows, and enterprise software solutions.

Build and maintain AI-powered go-to-market systems that automate sales, expansion, marketing, forecasting, handoffs, and post-sales workflows. Optimize processes, maintain CRM data quality, test and monitor automations, connect GTM tools, and translate operational needs into scalable technical solutions. The role uses AI and iPaaS tools to surface next-best actions, improve deal velocity, and support customer lifecycle operations.

Build and own AI safety systems for Realtor.com’s LLM-powered real-estate assistant. Responsibilities include developing Fair Housing and content-moderation classifiers, creating evaluation datasets and pipelines, measuring precision, recall, F1, and confusion matrices, integrating cloud guardrails, red-teaming for jailbreaks and prompt injection, managing Terraform infrastructure, and partnering with engineering, product, legal, and compliance teams to launch safe AI capabilities.

Own an AI portfolio within a business domain, identify and prioritize opportunities, and build agentic solutions from prototype through production. Establish reusable patterns, evaluations, traces, and human-in-the-loop controls for increasing autonomy. Partner with business leaders, measure portfolio-level impact, and build production-grade applications across frontend and backend systems. Mentor engineers, review designs and code, influence platform choices, and communicate effectively with operators, engineers, and executives.

Designs, develops, deploys, and scales advanced AI and machine learning models and systems. Leads research initiatives, architecture decisions, technical design reviews, and continuous improvement efforts. Collaborates with data scientists, software engineers, and product managers, while mentoring engineers and communicating strategies to executive stakeholders. Applies data mining, modeling, NLP, deep learning, and statistical techniques to solve complex product and systems problems.

Lead applied AI research focused on information retrieval, RAG, agentic workflows, ranking, reasoning, and evaluation. Build prototypes, golden datasets, quality metrics, regression detection, and experimentation frameworks; investigate failure modes and validate improvements through rigorous testing. Collaborate with AI and software engineers to productionize successful approaches and stay current with research in RAG, agents, LLM evaluation, and relevance modeling.

Design, develop, deploy, and support foundational AI systems, including foundation model training, LLM inference, agentic workflows, vector search, guardrails, evaluation, governance, and observability. Optimize large-scale AI systems for cost, latency, throughput, scalability, and hardware utilization. Architect multi-model orchestration pipelines, establish AI engineering standards, lead governance and design reviews, contribute to technical roadmaps, and mentor senior AI engineering leaders.

Design, develop, deploy, and support foundational AI systems, including foundation model training, LLM inference, agentic workflows, vector search, guardrails, evaluation, governance, and observability. Optimize large-scale AI systems for cost, latency, throughput, scalability, and hardware utilization. Architect multi-model orchestration pipelines, establish AI performance governance, lead design reviews, define technical roadmaps, and mentor senior AI engineering talent.

Design, develop, deploy, and support foundational AI systems, including model training, LLM inference, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. Optimize large-scale AI systems for cost, latency, throughput, scalability, and hardware utilization. Own complex system architecture, define reliability objectives, lead technical reviews, ensure security and compliance, contribute to strategic roadmaps, and mentor senior technical staff.

Own the implementation, hardening, and polish of game AI behaviors and systems for shipping. Collaborate with design, animation, and other disciplines to tune AI features and support the creative vision. Ensure reliable, stable enemy behaviors and enhance existing architecture. The role requires deep Unreal Engine and modern game AI experience, strong C++ skills, animation systems knowledge, and at least one shipped game title focused primarily on AI.

Designs, develops, and deploys enterprise-grade autonomous AI services and multi-agent workflows. Integrates MCP and A2A protocols, architectures hybrid control flows, secures tool execution, and builds scalable microservices. Applies SRE practices such as fallbacks, circuit breakers, audit trails, and observability. Requires advanced Python, cloud foundation model platforms, containerization, CI/CD, distributed systems, and production LLM deployment experience.

Design, develop, deploy, and support foundational AI systems, including LLM inference, agentic workflows, vector search, guardrails, evaluation, orchestration, and observability. Optimize model performance, scalability, latency, throughput, hardware utilization, and cost. Establish AI governance standards, lead technical reviews, shape long-term platform strategy, and mentor senior AI engineers. Collaborate with engineering, research, product, and program teams to deliver responsible AI products.

Design, develop, deploy, and optimize large-scale AI systems, including foundation models, LLM inference, agentic workflows, vector search, guardrails, and model evaluation. Lead AI architecture, orchestration, cost-performance governance, technical reviews, roadmap development, and responsible AI standards. Mentor senior technical staff and apply advanced optimization techniques to improve model scalability, latency, throughput, hardware utilization, and inference cost.

Design, build, test, deploy, and improve AI-enabled solutions for technology regulatory resilience and compliance. Apply generative AI, machine learning, and automation to connect regulatory obligations with controls, products, and evidence. Collaborate with engineers and regulatory experts across the full AI solution lifecycle while developing practical experience through Mastercard’s 18-month Launch Program.

Design, develop, deploy, and support foundational AI systems, including LLM inference, agentic workflows, vector search, model evaluation, governance, and observability. Optimize large-scale AI systems for cost, latency, throughput, scalability, and hardware utilization. Architect multi-model orchestration pipelines, establish AI engineering standards, lead technical reviews, and mentor senior engineering leaders while applying emerging research responsibly in production.