Advanced Data Analysis: Go beyond basic data exploration and delve into complex statistical analysis and modelling techniques using Python libraries like scikit-learn and TensorFlow. Conduct exploratory data analysis to gain insights and inform modelling decisions.
Machine Learning Expertise: Architect and implement sophisticated machine learning models to solve real-world and complex problems. Design and implement scalable machine learning pipelines and workflows.
Communication & Collaboration: Effectively translate technical findings into clear and actionable insights for technical and non-technical stakeholders. Collaborate with business teams to ensure data-driven solutions align with business objectives
Mentorship & Knowledge Sharing: Guide and mentor junior engineers, fostering a collaborative learning environment and sharing best practices within the team. Stay updated with the latest advancements in machine learning research and apply them to improve our solutions.
Skills
4-6 Years experience into AI/ML/Gen AI engineering.
Technical Expertise: Advanced proficiency in Python and its data science libraries (pandas, NumPy, scikit-learn, TensorFlow, etc.)
Experience with cloud platforms (AWS preferred)
Experience working with Large Language Models (Gen AI) and Natural Language Processing
Statistical and Machine Learning Acumen: Deep understanding of statistical concepts, machine learning algorithms, and their applications in various domains
Experience with data pre-processing, feature engineering, and model evaluation
Familiarity with version control systems (e.g., GitHub) and CI/CD pipelines.
Experience with containerization technologies such as Docker and Kubernetes
Problem-Solving & Innovation: A keen mind for identifying business problems and developing innovative data-driven solutions
Nice To Have
ERP or Supply Chain domain experience
Identify potential business areas that can leverage AI, ML or GenAI paradigm and build necessary models.
