Key Responsibilities:
- Lead the end-to-end design and development of machine learning models, from data preprocessing to deployment and monitoring in production.
- Architect and implement scalable MLOps pipelines using Azure ML, Azure Databricks, MLflow, and CI/CD workflows.
- Design and deploy LLM-based solutions, including Retrieval-Augmented Generation (RAG) and prompt engineering for enterprise use cases.
- Collaborate with product managers, data engineers, and business stakeholders to align AI solutions with organizational goals.
- Create insightful dashboards and visualizations using Power BI for model monitoring and business reporting.
- Apply software engineering and design principles to ensure reusable, testable, and maintainable ML code.
- Stay up to date with advancements in AI/ML, especially in Generative AI, Agentic AI and LLMs, and evaluate them for practical application.
- Review team deliverables, provide constructive feedback, and drive technical decision-making and architecture reviews.
- Contribute to the organization’s AI/ML roadmap, guiding adoption of new tools, frameworks, and research-driven techniques.
- Mentor junior team members, conduct code reviews, and lead knowledge-sharing sessions.
Required Skills and Experience:
- 8+ years of experience in Machine Learning, Software Development, and Data Analytics.
- Proficient in Python, SQL, and C#, with a strong background in software engineering.
- Hands-on experience with Azure ecosystem (Azure ML, Azure Data Factory, Azure Functions).
- Strong experience in Azure Databricks, Spark, and distributed data processing.
- Expertise in LLM integration, RAG pipelines, and prompt engineering for generative AI applications.
- Deep understanding of statistical modeling, hypothesis testing, and data interpretation.
- Solid foundation in MLOps, model monitoring, drift detection, and pipeline automation.
- Strong understanding of software engineering and design patterns in ML system development.
- Proficient in Power BI for visualization and reporting