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)