Top Artificial Intelligence Jobs

Design, develop, and deploy AI-native software products that transform large-scale geospatial data into actionable intelligence. Build multi-agent workflows using orchestration frameworks and large language models, engineer tool-using and self-refining agents, develop data ingestion pipelines, and operate secure, scalable AI systems in production. Apply DevOps, reinforcement feedback mechanisms, and AI-assisted development practices while contributing to engineering standards and documentation.

Own an AI portfolio across a business domain, identify and prioritize opportunities, and personally build production agentic solutions end to end. Establish reusable patterns, evaluations, human-in-the-loop controls, and measurable business outcomes. Partner with functional leaders, earn stakeholder trust, guide platform choices, and improve team capability through mentoring, design and code reviews, and interviewing. Build reliable, secure, scalable applications around AI systems across frontend and backend layers.

Build and deploy end-to-end AI solutions embedded within business teams. Identify high-value problems, rapidly prototype agentic applications, integrate AI with business workflows, and measure business impact. Partner with principal engineers and platform teams on architecture, drive stakeholder adoption, communicate across technical and business audiences, and mentor earlier-career builders. The role requires hands-on full-stack prototyping, API integration, production deployment, and ownership from discovery through adoption.

Lead and architect large-scale AI and automation programs, set Agile delivery models, manage end-to-end delivery from concept to production, oversee AI-specific risks and governance, influence senior leadership on AI strategy, and drive innovation and adoption across the organization.

Develop and test integration services, automated recompilation tools, data-mapping scripts, microservices, and software automation. Recompile legacy code into Java, create Helm Charts for Kubernetes deployments, execute integration test cases, and collaborate in an Agile environment. The intern will also support data security, privacy, compliance, and operational processes.

Administer and support Microsoft 365 Copilot, Copilot Studio, AI agents, Microsoft Entra ID, Teams dependencies, and Microsoft 365 tenant services. Responsibilities include configuration, licensing, deployment, governance, security, compliance, monitoring, troubleshooting, user provisioning, agent lifecycle management, integrations, audits, and platform health assessments. The role partners with AI, security, infrastructure, and business stakeholders to expand enterprise AI adoption while maintaining platform stability and data protection.

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.

Architect and govern enterprise Azure AI Foundry environments, Azure OpenAI implementations, model access patterns, evaluation frameworks, deployment standards, and AI governance practices. Design secure, scalable, production-grade generative and agentic AI platforms; optimize performance, reliability, and costs; and collaborate with business, cloud, cybersecurity, architecture, and data teams. Translate business needs into technology solutions while supporting compliance, data protection, and enterprise AI adoption.

The Staff AI Product Manager will drive product strategy for GPU offerings, conduct market analysis, collaborate with teams for product development, and advocate for customer needs. Responsible for managing product lifecycle and utilizing data-driven approaches for product success.

Designs and delivers production agentic AI platforms and large language model services for Finance workflows. Responsibilities include building agents, tool integrations, retrieval and context-engineering systems, secure APIs, and cloud-native AWS services using containers, serverless computing, and event-driven architecture. The role also optimizes performance and cost, establishes evaluation and observability practices, troubleshoots distributed systems, and collaborates on technical standards, documentation, and code quality.

Design and build scalable, reliable, observable, and well-tested backend or distributed systems. Use AI-assisted development tools to code, test, document, and design while validating generated outputs. Contribute to architecture and technical direction, apply systems thinking across data models, APIs, and service boundaries, and mentor peers. The role requires collaboration across Product, UX, and Engineering in a hybrid Austin environment.

Design, develop, deploy, and support foundational AI systems, including model training, LLM inference, similarity search, guardrails, evaluation, governance, and observability. Optimize large-scale AI systems for scalability, latency, throughput, hardware utilization, and cost. Establish technical vision and roadmaps, apply emerging research in production, and lead engineering teams while influencing cross-functional stakeholders.

As a Director, Data Scientist - Generative AI Systems, you will lead a team to develop AI products using NLP and generative AI technologies, partnering with cross-functional teams. Your role involves leveraging open-source programming and machine learning to convert customer data into actionable insights and enhance customer interactions with financial services.

Lead and grow an AI engineering team to design, build, deploy, and support large-scale, responsible AI systems (foundation model training, LLM inference, similarity search, guardrails, evaluation, observability). Make strategic build-vs-buy decisions across open-source and SaaS tools, optimize LLM performance for cost/latency/throughput, contribute to technical vision and roadmap, and mentor engineers to advance AI capabilities across the company.

In this role, you will lead AI projects, collaborating with cross-functional teams to develop and deploy advanced AI solutions, focusing on LLM inference and model optimization.

This role involves partnering with teams to develop AI-powered technology, advancing applied research in AI, and delivering impactful models. Candidates will work on foundational models and leverage new technologies to optimize customer interactions with banking services.

The Senior Lead AI Engineer will develop and support AI software components, partner with cross-functional teams, and enhance AI systems performance. Responsibilities include designing, deploying, and optimizing AI models for improved banking services.

Lead end-to-end design, implementation, deployment, and operation of production AI/ML-enabled full-stack applications. Build LLM-powered features, design scalable backend services and data models, deploy on Azure, implement CI/CD and observability, and mentor engineers while ensuring secure, compliant integration into clinical workflows.

Lead design and delivery of scalable, secure production ML and LLM-based systems. Provide technical guidance, build production-quality code, oversee ML architecture, monitoring, MLOps, AI agents and RAG solutions, and advise executives to improve operational workflows and model reliability.

Design and deliver end-to-end AI/ML solutions for Oncology R&D, working with multimodal datasets. Partner with scientists and product leaders to prototype, iterate, and deploy decision-relevant models, document methods for transparency, and ensure solutions are scientifically grounded and usable.

Develop, validate, and deploy ML/AI-driven cheminformatics models and workflows to predict molecular properties and inform hit-to-lead and lead optimization. Collaborate with medicinal chemists, biologists, and data scientists to integrate computational solutions, pilot new methods, and deliver actionable compound prioritization and ADME predictions with measurable impact.

Lead design and prototyping of AI-enabled solutions for Oncology R&D. Develop data pipelines, feature engineering, analytics, and ML models on healthcare data. Partner with scientific and product teams to deliver usable tools, emphasize rapid iteration and production-readiness, and contribute to engineering standards and documentation.

Develop, deploy, and maintain ML models and cheminformatics workflows to predict molecular and ADME properties. Process and analyze chemical/biological datasets using cheminformatics toolkits, build SAR and QSAR models, optimize data pipelines, and collaborate with cross-functional teams to integrate AI-driven approaches into drug discovery. Ensure data integrity, reproducibility, and model monitoring.

Lead Oncology AI product strategy and a cross-functional AI engineering team to translate scientific needs into scalable AI solutions across discovery, clinical development, and real-world evidence. Define roadmaps, governance, success metrics, stakeholder engagement, and adoption to move AI initiatives from pilots to sustained impact.

Provides senior technical leadership for enterprise voice, unified communications, and collaboration platforms. Designs and supports Cisco UC, Webex, Ribbon SBC, SIP, carrier, PSTN, E911, and endpoint solutions. Performs interoperability testing, packet and protocol analysis, troubleshooting, service assurance, compliance management, and telecom inventory administration. Drives automation for provisioning, reporting, endpoint management, and operational workflows while partnering with security, compliance, risk, and legal teams.