IN_RBAI_AI_ML_Expert_IN.

Key Responsibilities:

1. 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.
2. Model Development:
   • Develop, train, and validate machine learning models to address business challenges.
   • Implement deep learning algorithms where applicable for advanced analytics.
3. 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.
4. 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.
5. Collaboration:
   • Work closely with cross-functional teams, including data engineers, business analysts, and product Planning Team, to understand project requirements.
6. 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.

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

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.