Machine Learning Engineer

**MLOps/LLMOps Engineer** with a strong background in **continual learning, CI/CD, and cloud infrastructure** , particularly on **Azure, GCP, and AWS** . The ideal candidate will have extensive hands\-on experience in **Python and ML libraries** (Scikit\-learn, TensorFlow, PyTorch), and a proven track record in deploying, monitoring, and optimizing machine learning and large language model pipelines. **Core Responsibilities \& Skills:** **1\. MLOps \& LLMOps Pipeline Development** * Design, implement, and automate **end\-to\-end ML/LLM pipelines** with a focus on **continual learning, model retraining, and A/B testing** . * Integrate **CI/CD workflows** for seamless model deployment, versioning, and rollback strategies. **2\. Cloud \& Infrastructure Expertise** * Strong hands\-on experience with **Azure, GCP, and AWS** cloud platforms, including managed services for ML (Azure ML, Sagemaker, Vertex AI). * Proficiency in **Docker, Kubernetes** , and cloud\-native architectures for scalable, containerized deployments. **3\. ML \& LLM Tools \& Frameworks** * Expertise in **ML pipeline tools** : MLflow, Airflow, Kubeflow, Sagemaker, Azure ML. * Experience with **LLM tools and frameworks** : LangChain, LlamaIndex, Hugging Face, OpenAI/Azure OpenAI APIs. * Hands\-on experience with **vector databases** : Pinecone, Weaviate, Chroma, Qdrant. **4\. Monitoring, Optimization \& Scalability** * Implement **monitoring and observability** using tools like Prometheus, Grafana, ELK, and Datadog. * Optimize **GPU compute, inference latency, and model serving** for high\-performance, scalable architectures. **5\. Programming \& Collaboration** * Strong **Python** skills and familiarity with ML libraries (Scikit\-learn, TensorFlow, PyTorch). Collaborate with data scientists, engineers, and product teams to deliver robust, production\-grade ML/LLM solutions. -