Role Summary

Lead the design, development, and deployment of scalable Machine Learning (ML) solutions. Drive architecture decisions, mentor the engineering team, and integrate advanced Artificial Intelligence (AI) capabilities, including Large Language Models (LLMs), into enterprise workflows.

Must-Have Requirements

• Machine Learning: In-depth understanding of supervised, unsupervised, and reinforcement learning techniques, model evaluation methodologies, and feature engineering.
• Deep Learning: Proficiency with TensorFlow, PyTorch, Keras; practical experience with Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs).
• Programming: Expert proficiency in Python (including libraries like NumPy, Pandas, scikit-learn); exposure to R is acceptable.
• Big Data Technologies: Hands-on experience with Spark (Databricks preferred); familiarity with Hadoop and Kafka.
• Cloud Platforms: Experience with Azure, AWS, or GCP, including their ML services, data storage, and compute capabilities, coupled with strong MLOps experience.
• Data Warehousing & Databases: Strong command of SQL, NoSQL databases, and data modeling principles.
• Drift Detection & Monitoring: Practical experience with identifying and monitoring model drift, implementing automated alerts.
• Data Governance: Experience in implementing data governance frameworks, including lineage tracking, metadata management, and ensuring compliance.
• MLOps Pipelines: Ability to architect scalable MLOps pipelines utilizing tools such as Azure ML, MLflow, Databricks Asset Bundles (DAB), and adhering to CI/CD/CT practices.

Good-to-Have Skills

• Experience in designing and deploying LLM solutions, including Retrieval-Augmented Generation (RAG), Agentic AI, Multi-turn Conversational Processing (MCP), and prompt engineering.
• Ability to build Power BI dashboards for model monitoring and business Key Performance Indicator (KPI) reporting.
• Solid foundation in statistics, hypothesis testing, and data interpretation.
• Application of software engineering principles to develop reusable, testable, and maintainable ML code.
• Keen interest and ability to stay updated with advancements in Generative AI, Agentic AI, and LLMs, and assess their practical adoption.
• Experience mentoring junior engineers, conducting code reviews, and facilitating technical knowledge-sharing sessions.
• Relevant certifications, such as Microsoft Certified: Azure Data Scientist Associate, are a plus.