IN_RBAI_AI_ML_Expert_IN_2026

Job Description Key Responsibilities: Data Analysis and Preprocessing: Gather, clean, and preprocess structured and unstructured data for analysis. Perform exploratory data analysis (EDA) to uncover trends, patterns, and actionable insights. Model Development: Develop, train, and validate machine learning models to address business challenges. Implement deep learning algorithms where applicable for advanced analytics. Data Visualization: Create interactive dashboards and reports to visualize insights using tools like Tableau, Power BI, or Python libraries (e.g., Matplotlib, Seaborn, Plotly). Communicate results and recommendations effectively to stakeholders. System Integration and Deployment: Integrate ML models into production environments and ensure their scalability and performance. Use tools like TensorFlow Serving, Flask, FastAPI, or Docker for model deployment. Collaboration: Work closely with cross-functional teams, including data engineers, business analysts, and product Planning Team, to understand project requirements. Continuous Learning and Improvement: Stay updated with the latest trends, technologies, and methodologies in data science, machine learning, and AI. Optimize existing models and systems for improved performance and accuracy. Qualifications min. 1 (BE)  to 6 years relevant job experience B.E in AI or CS or E&C or EEE or Mechatronics Certifications in AI/ML or data analytics (e.g., Google Data Analytics, TensorFlow Developer, AWS Certified Machine Learning). Experience with natural language processing (NLP), computer vision, or reinforcement learning Additional Information Required Skills and Qualifications: Educational Background: Bachelor’s degree in Computer Science, Data Science, Statistics, Mathematics, or related fields. Technical Skills: Proficiency in Python, R, or Julia. Experience with machine learning libraries and frameworks (e.g., TensorFlow, PyTorch, Scikit-learn). Strong knowledge of SQL and NoSQL databases. Familiarity with big data tools like Spark, Hadoop, or Databricks is a plus. Knowledge of cloud platforms like AWS, Azure, or Google Cloud for data and model deployment. Analytical Skills: Strong understanding of statistics, probability, and data structures. Ability to handle large datasets and apply algorithms efficiently. Soft Skills: Excellent problem-solving and critical-thinking skills. Strong communication and teamwork capabilities. Ability to translate business requirements into technical solutions.