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.