Description

You will design, architect, and execute end-to-end machine learning pipelines and generative AI solutions.

Responsibilities

• Train and optimize models for new data providers and enhance existing LDA, RAG, and forecasting architectures.
• Migrate data pipelines from Azure Synapse to Azure Databricks to improve scalability and efficiency.
• Develop and implement time series models including ARIMA, Prophet, and LSTMs for demand prediction and trend analysis.
• Deploy production-ready RAG models and upgrade Retrieval QA architectures using Langchain.
• Implement MLOps best practices for model deployment using Azure ML, AKS, and Azure Functions.
• Optimize embedding storage and retrieval processes within Azure Blob Storage to address time and space complexity.

Required Skills

• 5+ years of experience in Data Science, Machine Learning, Deep Learning, and Gen AI.
• Proficiency in Python including NumPy, Pandas, TensorFlow, PyTorch, Statsmodels, Scikit-learn, and Hugging Face.
• Hands-on experience with Azure Cloud services: Databricks, ML, Synapse, Blob Storage, and AKS.
• Expertise in NLP, RAG architectures, Langchain, and multi-agentic generation.
• Experience building and deploying forecasting, anomaly detection, and clustering models.
• Strong knowledge of CI/CD pipelines using Azure Synapse, Databricks, Google Vertex AI, or AWS.
• Practical experience with Docker, Kubernetes, MLflow, and FAISS.
• Ability to design end-to-end pipelines covering extraction, preprocessing, feature engineering, and deployment.
• Experience leading teams and managing project delivery.

Preferred Skills

• Experience managing GitHub repositories and cloud computing resources for version control and testing.
• Proven track record of developing and rolling out proof-of-concept solutions to clients.

Key Skills

Education

Any Graduate