Top AI & Machine Learning Jobs

Build, deploy, scale, and maintain production machine learning models and platforms. Responsibilities include developing ML algorithms, optimizing cloud-based infrastructure, constructing data pipelines, operating distributed systems, automating testing and deployment, monitoring models, and applying responsible and explainable AI practices. The role requires collaboration with product, data science, and cross-functional Agile teams.

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

Design, develop, deploy, and support foundational AI systems, including foundation model training, LLM inference, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. Optimize model performance, cost, latency, throughput, and hardware utilization across scalable production systems. Own complex AI architecture, define reliability objectives, lead technical reviews, ensure security and compliance, contribute to long-term roadmaps, and mentor engineering staff.

Build, deploy, optimize, and maintain production machine learning models and infrastructure. Responsibilities include developing ML applications, designing data pipelines, operating distributed systems and cloud platforms, monitoring and retraining models, automating testing and deployment, and applying responsible AI practices. The role collaborates with Product and Data Science teams to deliver scalable solutions using frameworks such as PyTorch or TensorFlow and platforms including AWS, Kubernetes, Spark, and Ray.

Design, build, deploy, retrain, and monitor production machine learning models and components. Develop scalable ML infrastructure, data pipelines, cloud architectures, and distributed systems using Python and related technologies. Collaborate with Product and Data Science teams, automate testing and deployment, apply CI/CD practices, and ensure model governance, security, responsible AI, and explainability across enterprise platforms.

Architect, build, and deploy enterprise-scale generative AI and autonomous agent systems for HR technology. Lead multi-agent workflows, full-stack cloud applications, distributed microservices, and technical initiatives while ensuring security, governance, compliance, and operational excellence. Partner with product managers, architects, and risk teams; establish engineering standards; mentor developers; evaluate emerging technologies; and reduce technical debt. The role requires expertise in programming, databases, APIs, cloud platforms, CI/CD, infrastructure as code, RAG, vector search, and LangChain or LangGraph.

Leads applied AI research and develops foundation models from design through deployment. Partners with data scientists, engineers, and product leaders to deliver scalable AI products, defines long-term research agendas, mentors applied scientists, collaborates with academia, and advances methods including deep learning, self-supervised learning, model optimization, robustness, explainability, and RLHF.

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

Build, deploy, scale, and monitor production machine learning models and platforms. Responsibilities include developing ML applications, optimizing models and infrastructure, creating data pipelines, operating distributed systems and cloud-based services, managing Kubernetes environments, automating testing and deployment, and applying responsible AI practices. The role collaborates with product, data science, and Agile engineering teams to deliver reliable ML solutions at scale.

Design, develop, deploy, and support foundational AI systems, including large language model inference, foundation model training, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. Optimize AI systems for scalability, cost, latency, throughput, and hardware utilization. Own complex system architectures, define reliability objectives, lead technical reviews, ensure security and compliance, and mentor engineering colleagues.