Lead the design, development, and deployment of ML solutions at scale. Drive architecture, mentor the team, and integrate advanced AI (including LLMs) into enterprise workflows.

• Machine Learning: Deep understanding of supervised, unsupervised, and reinforcement learning, model evaluation, and feature engineering.
• Deep Learning: Proficiency with TensorFlow, PyTorch, Keras; hands-on with CNNs, RNNs.
• Programming: Expert in Python (NumPy, Pandas, scikit-learn, etc.); R exposure acceptable.
• Big Data Technologies: Practical experience with Spark (Databricks preferred); familiarity with Hadoop/Kafka.
• Cloud Platforms: Azure or AWS or GCP (ML services, data storage, compute), with strong MLOps exposure.
• Data Warehousing & Databases: Strong SQL, NoSQL, and data modeling.
• Drift Detection & Monitoring: Hands-on experience with model drift detection, monitoring, and automated alerts.
• Data Governance: Practical experience implementing governance frameworks, lineage tracking, metadata management, and compliance.
• Architect scalable MLOps pipelines using Azure ML, MLflow, Databricks Asset Bundles (DAB), and CI/CD/CT

• Design & deploy LLM solutions (RAG, Agentic AI, MCP, prompt engineering).
• Build Power BI dashboards for monitoring models and reporting business KPIs.
• Strong grounding in statistics, hypothesis testing, and data interpretation.
• Apply software engineering principles for reusable, testable, and maintainable ML code.
• Stay up to date with Generative AI, Agentic AI, and LLMs and assess practical adoption.
• Mentor juniors, review code, and conduct technical knowledge-sharing sessions.
• Certification: Microsoft Certified: Azure Data Scientist Associate (nice to have).