Top AI & Machine Learning Jobs

Operate, automate, and improve enterprise AI platforms (Generative AI, LLMs, RAG, Agentic AI). Implement SRE practices, MLOps/LLMOps, cloud-native infrastructure, IaC, observability, security controls, incident response, and scalability. Collaborate with AI/ML and platform teams and mentor junior engineers to ensure production readiness and reliability.

Lead full-stack development of internal tools and automation frameworks using Python and modern front-end tech. Build AI-enabled testing and quality intelligence with LLMs and ML models, integrate services with cloud platforms (AWS/Azure) and CI/CD, and collaborate across engineering, platform, and AI teams to deliver scalable, secure automation and testing solutions.

Lead enterprise architecture strategy and roadmaps for large-scale, secure, cloud-based systems. Drive solution design across data, AI/ML, platforms, integration, governance, and stakeholder alignment to productionize scalable intelligent solutions.

Lead design and implementation of scalable ML, deep learning, and generative AI solutions (NLP/LLMs), build production pipelines on cloud, create document-parsing models, collaborate cross-functionally, and publish research and patents.

Lead design, development, and scaling of enterprise AI/ML and generative AI platforms and agentic systems. Build and mentor globally distributed engineering teams, operationalize AI-enabled SDLC (AIDLC), drive productionization, observability, and performance, and align AI initiatives with business outcomes while championing responsible AI governance and cross-functional delivery.

Design, build, and optimize scalable data pipelines and ML workflows on Azure/Databricks using Python and Spark. Operationalize ML models, ensure data quality and governance, automate deployments, and collaborate in Agile teams to support model training, inference, and monitoring.

Design and build scalable backend services (Python/FastAPI), responsive frontends (React), and enterprise integrations. Lead development of agentic AI/LLM features, multi-agent workflows, vector search/RAG, and cloud-native deployments using Docker, Kubernetes, and Azure. Collaborate with architects and product teams to ensure reliability, security, and observability.

Build and operate cloud infrastructure and IaC for AI/ML agent workloads. Design CI/CD for containerized services, ensure security, observability, cost/performance optimization, incident/runbook governance, and partner with ML teams for safe LLM rollouts.

Lead design and delivery of enterprise GenAI, LLM, RAG, and Agentic AI solutions. Build scalable ML pipelines, deploy models to production, integrate with frontend/backend systems, ensure responsible AI, monitor model performance, and mentor engineers while partnering with product and data teams.

Design, develop, deploy, and maintain scalable ML models and pipelines (prediction, forecasting, recommendations, anomaly detection). Collaborate with data and platform teams for data engineering, MLOps, monitoring, and integration into enterprise applications while following Responsible AI and governance practices.