1-2 years experience in python and ml frameworks (tensorflow, pytorch, scikit-learn, or equivalent). Hands-on experience deploying ml models in enterprise environments. Experience integrating and operationalizing llms and rag architectures. Experience with aws, azure, or gcp in enterprise contexts. Strong knowledge of apis, distributed systems, and scalable architecture. Design and implement end-to-end ml and genai systems, from development to production deployment. Develop scalable architectures for rag-based applications, llm integrations, and advanced inference pipelines. Productionize machine learning and large language models, ensuring reliability, observability, and maintainability. Build and optimize data and model pipelines, including training, evaluation, deployment, and monitoring. Design model serving solutions and api integration patterns within enterprise systems. Optimize model performance for latency, throughput, cost efficiency, and resilience. Implement automated testing, evaluation frameworks, and guardrails for ml and llm systems. Collaborate with data scientists to transition prototypes into production-grade solutions. Engage directly with client technical teams across industries and technology stacks. Advanced english proficiency