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.