Python_ML Ops_Framework Engineer

Educational Qualifications:

B.E./B.Tech/M.Tech/MCA in Computer Science, Information Technology, or a related field.

Advanced degree in Data Science, Machine Learning, or Artificial Intelligence is a plus.

Experience:

3+ years of experience in IT, with at least 2+ years in MLOps and machine learning model deployment.

Hands-on experience with Databricks for scalable data processing, model training, and orchestration.

Experience working with Large Language Models (LLMs) such as OpenAI, Azure OpenAI, Hugging Face Transformers, or similar.

Core Responsibilities:

Design, build, and maintain end-to-end ML pipelines using Databricks, MLflow, and Azure Machine Learning.

Implement CI/CD pipelines for ML models, ensuring reproducibility, version control, and automated deployment.

Operationalize LLM-based solutions, including prompt engineering, fine-tuning, and inference optimization.

Monitor and manage model performance, drift, and retraining workflows.

Collaborate with data scientists, data engineers, and DevOps teams to ensure seamless integration and delivery.

Technical Skills:

Proficiency in Python and scripting for automation and orchestration.

Strong understanding of MLOps tools: MLflow, DVC, Kubeflow, Airflow, or similar.

Experience with containerization (Docker) and orchestration (Kubernetes).

Familiarity with Azure DevOps, Git, and CI/CD pipelines for ML workflows.

Knowledge of data governance, model monitoring, and compliance best practices.

Desirable Skills:

Exposure to LLM fine-tuning, embedding generation, and vector databases (e.g., FAISS, Weaviate, Pinecone).

Experience with Databricks Unity Catalog, Delta Live Tables, and Feature Store.

Understanding of data security, access control, and cost optimization in cloud environments.

Soft Skills:

Strong problem-solving and debugging skills.

Excellent communication and collaboration abilities.

Ability to work in a fast-paced, agile environment and manage multiple priorities.

MLOps-Python API Engineer