Senior Machine Learning Engineer

## 1. Programming & Software Engineering - Strong proficiency in Python - System design - Git version control ## 2. Machine Learning & Deep Learning - Production-level Machine Learning Engineering experience - Machine Learning & Deep Learning fundamentals - PyTorch and TensorFlow ## 3. Generative AI & LLM Engineering - LLMs and Generative AI - Retrieval-Augmented Generation (RAG) - LangGraph - MCP (Model Context Protocol) ## 4. Data Engineering & Feature Management - Pandas, NumPy, SciPy for data processing - Data versioning and dataset management - Feature engineering and feature stores ## 5. MLOps & Model Lifecycle - CI/CD for ML (pipelines, deployment strategies) - Model versioning - Model monitoring and observability (latency, throughput, logging, tracing) - Data drift and concept drift detection ## 6. Infrastructure & Platform Engineering - Docker and containerization - Kubernetes - On-premise infrastructure experience - Hardware knowledge (CPU/GPU, memory, storage optimization) ## 7. Data Stores & Messaging Systems - Redis - MongoDB and PostgreSQL - Message queues (RabbitMQ) ## 8. Preferred Skills (Any of the following) - Model serving frameworks (Triton, SGLang, vLLM) - Vector database (Qdrant) - Experiment tracking tool (MLflow) - CI/CD tools (GitLab CI) - Networking (NGINX)

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