Top Artificial Intelligence Jobs

Lead design and delivery of AI and blockchain-enabled integration and platform architectures. Analyze requirements, create technical designs and cloud strategies (Azure), direct large projects, validate outcomes with clients, coach and manage diverse teams, perform feasibility and business process modeling, and drive enterprise architecture and system integration initiatives to support audit automation and strategic technology roadmaps.

Lead data science initiatives within Data and Analytics Engineering to build predictive models, maintain data pipelines and lakes, apply ML/DL and generative AI, ensure data quality, coach teams, manage project timelines, and collaborate with stakeholders to drive data-driven business growth.

Lead QA for AI-enabled software: design and execute test strategies, identify and resolve defects, perform functional/compatibility and model-output testing, automate tests (e.g., Selenium), analyze data to inform recommendations, support post‑go‑live issues, and mentor junior team members while managing client expectations.

Design, code, and test enterprise applications using Kotlin, Rust, and SwiftUI. Implement ATDD and CI/CD, API management and microservices, manage application lifecycle, perform code reviews and debugging, collaborate with clients, and drive business process improvements.

Lead and manage a software engineering team to design, build, test, and deploy AI-enabled, scalable applications. Own project planning, budgeting, delivery, code quality, CI/CD, observability, and cross-functional collaboration while coaching engineers and driving innovation.

Lead strategy and execution of technology, data, and AI for Clinical Development. Architect scalable platforms and analytics, industrialize AI/ML use cases, ensure regulatory compliance, govern vendor and product lifecycles, and lead global multidisciplinary teams to accelerate clinical evidence generation and decision-making.

Lead and grow an AI/ML engineering team to design, build, and deploy production-grade ML/AI solutions (NLP, NLU, deep learning, CV, ASR). Oversee architecture, MLOps, experiments, model monitoring/versioning, cross-functional collaboration, and ethical AI practices to translate research into scalable business solutions.

Design, build, and deploy production-grade AI/ML models, agentic workflows, and data pipelines using Python, Databricks, LangChain/LangGraph, and Azure. Collaborate with cross-functional teams to translate requirements into scalable LLM applications, implement feature engineering and validation frameworks, and embed responsible AI practices including fairness, security, and monitoring.

Develop and implement machine learning solutions using LLMs and the LangChain framework. Ensure production quality code and utilize various ML techniques and containerization tools.

As a Staff AI Scientist, you will lead AI projects, design deep learning models using multimodal data, and ensure regulatory compliance in healthcare AI products.

Senior Full Stack Software Engineer role at Upside focusing on driving quality software development and enhancing consumer experiences through collaboration and technical choices.

As an AI Strategist, you will drive strategic deployment and growth of Hebbia’s AI platform in top financial institutions, leveraging domain expertise and client relationships.

The AI Strategist will manage high-value client relationships, drive commercial growth, and translate complex business needs into AI solutions for Hebbia's financial clients.

The AI Strategist will drive AI deployment and value creation across financial institutions, optimizing workflows and enhancing customer experience while building partnerships and expertise in the banking sector.

As an AI Strategist at Hebbia, you will drive strategic deployment of AI solutions in finance, build customer relationships, and enhance workflows for value creation.

Lead an AI engineering team to design, build, deploy, and optimize large-scale generative AI systems. Oversee foundation model training, LLM inference, similarity search, guardrails, evaluation, observability, and make strategic build-vs-buy decisions. Drive technical vision, roadmap, and talent development while ensuring scalable, cost-efficient, and responsible AI production systems.

Lead design, develop, deploy, and support foundational AI systems (LLM training/inference, similarity search, guardrails, observability). Partner cross-functionally to optimize scalability, cost, latency, and throughput, apply state-of-the-art research to production, and shape long-term AI platform roadmap.

Lead product strategy and lifecycle for Physical IP and CPU products targeting Edge AI. Define roadmaps, translate market and customer insights into engineering requirements, drive competitive positioning, and engage Tier 1 customers to secure design wins. Collaborate cross-functionally with engineering, sales, marketing, and executives to influence CPU roadmap, deliver commercial cases, and ensure customer success.

Design and implement scalable platform services, runtimes, and developer tooling for deploying and managing AI workloads on Arm-based edge devices. Build abstractions, observability, and performance optimizations across heterogeneous hardware while collaborating with architects and AI engineers to produce robust, maintainable systems and SDKs for on-device AI.

Design, build, and operate scalable, secure multi-cloud infrastructure and AI-focused platforms. Lead IaC automation (Terraform/Ansible/CloudFormation), enable MLOps workflows for model training and inference, apply AI-assisted engineering to reduce toil, drive platform strategy, deliver large-scale projects, and mentor engineers.

Lead design and delivery of agentic, LLM-powered backend and platform AI systems. Build and operate Java and Python microservices, implement orchestration, RAG, prompt management, observability, and cost telemetry. Drive architecture, integration with platform and vendor components, enforce engineering standards, and ensure Responsible AI, security, and production reliability at enterprise scale.

Design, develop, and deploy agentic AI workflows and LLM-based automations for clinical and business use. Build scalable RAG solutions, vector DB integrations, and production-grade Python services. Implement AI orchestration frameworks, ensure MLOps/LLMOps, monitor observability, and collaborate with product, clinical, security, and engineering teams to deliver secure, compliant healthcare AI capabilities.

Design, build, and operate scalable AWS cloud infrastructure and CI/CD for AI/ML platforms. Implement IaC (Terraform/CloudFormation), container orchestration (Docker, Kubernetes/EKS), monitoring, security, and observability while collaborating with AI and data teams to enable model deployment and MLOps workflows.

The Data Management and Automation Analyst manages data repositories, conducts analytics, implements system enhancements, and leverages AI to streamline processes while ensuring compliance and data governance.

Lead administrative, academic, and operational management of a new grant-funded Master of Science in Applied Computing: coordinate curriculum, faculty, research, admissions, budgeting, grant compliance, student support, and external partnerships to grow and sustain the program.