MLOps Engineer

Hands on Experience in MLOps or AI integration projects.

Hands -on experience in DevOps or software engineering.

Hands-on with RAG architectures for LLMs.

Hands-on with Python, REST APIs, Bash scripting.

Experience with Docker, containerization, and CI/CD workflows.

Familiarity with cloud platforms (Azure, AWS, or GCP).

Knowledge of AI pipeline tools (MLflow, FastAPI, Airflow, LangChain, etc.).

Experience with at least one vector database (e.g., FAISS, Weaviate, and Chroma).

Understanding of deploying AI services securely and efficiently.

Experience with Agile and DevOps software development principles/methodologies and working on

teams focused on delivering business value.