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)