Job Overview:

Job Overview

• Designs, implements, and maintains advanced analytics and machine learning environments to solve complex business challenges.
• Leads development of enterprise-scale ML, NLP, Generative AI, and MLOps solutions aligned with business and architecture goals.

Key Responsibilities

• Architect and build scalable machine learning pipelines using Databricks (PySpark, MLflow) and Snowflake (Snowpark, Streamlit, Cortex AI).
• Design and implement Retrieval-Augmented Generation (RAG) and LLM-based solutions for enterprise data and document intelligence.
• Develop supervised (classification, regression) and unsupervised machine learning models.
• Implement MLOps practices including model versioning, CI/CD pipelines, automated testing, and monitoring.
• Deploy and scale ML/NLP models into production environments using Azure DevOps and CI/CD pipelines.
• Develop and optimize data pipelines for structured and unstructured data using Azure Data Factory, PySpark, and SnowSQL.
• Tune and optimize Databricks and Snowflake environments for ML and AI workloads.
• Establish and maintain analytics environments for data science, BI, and enterprise data warehousing.
• Ensure ML solutions align with enterprise architecture, standards, and business objectives.
• Collaborate with cross-functional teams and stakeholders to define ML solutions and communicate architecture decisions.
• Identify gaps in standards and define best practices for coding, testing, and documentation.
• Drive innovation by evaluating emerging ML, LLM, and cloud technologies.
• Mentor team members on ML best practices and cloud-native data science workflows.
• Ensure proper handling of sensitive data including PHI in compliance with regulations.

Skills And Expertise

• Strong expertise in Snowflake architecture, performance tuning, and SQL optimization.
• Experience with ETL/ELT tools such as Azure Data Factory and Coalesce.
• Proficiency in Python, SnowSQL, and Snowflake APIs/Snowpark.
• Experience with LLM development (RAG, chatbots, summarization using LangChain or LlamaIndex).
• Strong understanding of data warehousing, dimensional modeling, and cloud platforms (Azure, AWS, GCP).
• Hands-on experience with Databricks, Azure Machine Learning, and Snowflake integrations.
• Proficiency in MLOps tools such as MLflow, Azure DevOps, and CI/CD pipelines.
• Familiarity with BI tools such as Power BI, Tableau, and SSRS.
• Strong background in machine learning, deep learning, and advanced statistical techniques.
• Experience with deep learning frameworks such as TensorFlow or PyTorch.
• Strong analytical, problem-solving, and communication skills.

Qualifications

• 7+ years of experience in machine learning and data science.
• 3+ years of experience in ML solution architecture and pipeline development in complex environments.
• 2+ years of experience in MLOps practices and production model lifecycle management.

Preferred Qualifications

• Experience in healthcare or regulated environments handling PHI data.
• Experience with enterprise-scale AI/ML deployments.
• Strong experience with cloud-native AI/ML architectures and automation.