Top AI Engineer Jobs

Lead the development of AI safety strategies for autonomous vehicles while ensuring compliance with industry standards, focusing on ML model validation and deployment in driving systems.

The Senior AI/ML Engineer will design and implement scalable ML infrastructure solutions, mentor junior engineers, and own key technical projects.

Design and implement scalable cloud solutions for autonomous vehicles, lead projects from inception to completion, and mentor junior engineers.

As a Staff AI/ML Full-Stack Engineer, you will design and build end-to-end software products, overseeing both frontend and backend development, while mentoring junior engineers and leading complex technical projects.

Join the Inference team to implement features and improvements for model serving on a GPU platform, focusing on performance and reliability.

As an Optical Engineer, you will lead the assembly, integration, and testing of optical systems for spaceborne applications, collaborating with suppliers and managing production scaling.

The Platform Engineer will build AI tools, automate workflows, collaborate on product designs, and enhance Ibotta's platform using AWS and CI/CD processes.

As a Lead Software Engineer on the AI Platform Team, you will design and develop GenAI platforms, mentor peers, and integrate AI technologies into scalable solutions.

As a Senior AI Data Governance Engineer, you'll design governance for AI, manage ML/AI Ops pipelines, collaborate across cloud environments, and ensure data compliance.

The Lead Software Engineer will architect and develop a scalable AI platform, mentor peers, and integrate generative AI solutions into enterprise applications.

Lead a cross-functional team to deliver AI-powered banking products, focusing on developing and supporting AI software components and optimizing LLM technologies.

Lead design, build, and productionize AI/GenAI and ML voice-bot solutions across cloud platforms. Own end-to-end delivery including data pipelines, models, deployment, monitoring, and mentoring a small team while partnering with cross-functional stakeholders.

Lead design, build, and deploy scalable AI/ML platforms and full-stack applications. Responsibilities include model development and deployment, full-stack engineering (backend services, APIs, lightweight UIs), cloud-native architectures, MLOps, governance, technical leadership and mentorship, applied research in generative AI/NLP/vision, and operational excellence (observability, DR, performance tuning).

Lead design and hands-on implementation of scalable AI/ML systems for healthcare. Partner with cross-functional teams to define scope, deploy and monitor LLM/agent solutions, drive architecture and technical strategy, mentor engineers, and deliver production AI products that improve patient outcomes.

Lead design, development, deployment, and operationalization of AI/ML solutions (including LLMs and Generative AI) for healthcare. Provide technical leadership on chatbots, conversational AI, and NLP, ensure responsible AI and production reliability, and mentor engineering teams.

Architect, build, and deploy NLP, OCR, RAG and cloud ML solutions to modernize fax and telephony workflows; mentor teammates; collaborate with product and cross-functional teams; ensure secure, scalable production ML operations and monitoring.

Lead end-to-end AI/ML projects: research, prototype, optimize, and productionize ML and LLM solutions for healthcare. Mentor and recruit teams, run experiments and A/B tests, present results to leadership, and pursue AI research and IP.

Design, develop, test, and deploy generative AI/ML models (LLMs, RAG, agentic AI) into production using agent frameworks, containerization, CI/CD, and cloud platforms; analyze large datasets and optimize system performance.

Design, develop, and productionize NLP/ML/AI solutions (LLMs, NLU/NLG/SLU) for high-traffic consumer systems. Lead end-to-end model lifecycle: data ingestion, feature engineering, modeling, deployment, optimization, monitoring, and POCs. Establish best practices, prioritize work, and collaborate on scalable, voice-based bot platforms built on LLM frameworks.

Lead AI/ML Engineer to design, develop, and deploy AI-powered solutions across the full ML lifecycle. Own architecture, MLOps, monitoring, and scalability. Partner with product, data science, and platform teams, mentor junior engineers, and drive adoption of best practices for production ML and generative AI solutions.

Develop and deploy LLM-based solutions using LangChain, fine-tuning, and RAG. Write production-quality Python code (PEP8), use Docker and Git, apply traditional ML (XGBoost, K-means), and ensure testing and documentation.

Lead development of AI/ML solutions for healthcare using structured and unstructured data. Build and test deep learning and classical ML models, design experiments, perform statistical analysis and visualizations, ensure responsible AI compliance, produce documentation/patents/publications, and collaborate on MLOps pipelines and stakeholder integrations.

Lead design, development, deployment, and maintenance of AI-powered solutions (LLMs, Generative AI, traditional ML) for provider-facing products. Build chatbots and conversational NLP, integrate AI into cloud (preferably AWS), ensure responsible AI practices and compliance, provide technical leadership, mentor engineers, and collaborate with product and architecture teams.

Design, build, and deploy generative AI solutions (LLMs, RAG, agentic systems) using Python and .NET/C#. Develop APIs, containerize models, implement CI/CD, collaborate with stakeholders, and support production systems.

Lead design, training, and deployment of ML and generative AI/LLM solutions for clinical decision support. Translate business needs into production models, work with large-scale healthcare data and distributed computing, implement scalable MLOps pipelines, and collaborate with engineering and stakeholders to integrate AI into products.