Top AI & Machine Learning Jobs

Lead design and development of NLP, NLU, SLU, and computer vision ML/AI products for healthcare. Build and optimize GenAI models (quantization, RAG, LangChain, VectorDBs), manage model lifecycle, run complex POCs, deploy scalable solutions, and establish best practices across end-to-end ML development.

Design, prototype, and evaluate advanced AI/ML models (including generative and agentic systems). Optimize large-scale training pipelines, integrate solutions into production with data engineering and product teams, ensure ethical AI practices, publish research, and collaborate cross-functionally to deliver enterprise AI applications.

Lead backend-focused full-stack development for AI-assisted workflows: architect and deploy scalable, event-driven microservices on AWS, implement IaC and CI/CD, apply MLOps to integrate and monitor models in production, and improve performance, reliability, observability, and cost efficiency.

Design, implement, evaluate, and optimize conversational AI and search over medical content. Drive UiPath to Power Automate Desktop migrations, integrate AI/ML into PAD, explore reinforcement learning and self-improving techniques, monitor performance, ensure ethical AI, and collaborate cross-functionally to deliver production-ready solutions.

Lead engineering team to build cloud-native, AI-assisted full-stack applications. Architect event-driven microservices on AWS, implement IaC and CI/CD, apply MLOps and integrate ML models into regulated production systems while optimizing performance, reliability, observability, and cost.

Design, prototype, and benchmark advanced AI/ML algorithms and agentic systems; optimize large-scale training pipelines; integrate generative AI, RAG, and Azure OpenAI solutions into production; collaborate with cross-functional teams; ensure ethical, interpretable, and robust model deployment.

Design, prototype, and deploy advanced AI/ML models (including generative and agentic systems). Optimize training pipelines, benchmark models, integrate solutions into production on Azure, ensure ethical AI, and collaborate across teams to deliver enterprise AI applications.

Design, prototype, and deploy advanced AI/ML models (including generative and agentic systems). Conduct experiments, benchmarking, and optimize training pipelines. Integrate solutions with cloud and production systems (Azure, Databricks), ensure ethical AI practices, collaborate cross-functionally, and publish or contribute to research and open-source work.

Design, build, test, and deploy end-to-end full-stack AI/ML applications. Develop front-end experiences, backend services, APIs, and data pipelines. Productionize ML models, integrate Generative AI/LLMs, implement MLOps/DevOps, ensure security/compliance, deploy and scale on cloud platforms, and collaborate with cross-functional teams to deliver user-centric healthcare solutions.

Design, build, and deploy LLM/RAG-based AI solutions on cloud (preferably Azure). Develop generative AI models, APIs (FastAPI), CI/CD pipelines, containerized deployments, and production monitoring. Mentor engineers, resolve production issues, ensure code quality and security, and collaborate on data preprocessing and model validation.