Gen AI

Key Responsibilities

1. GenAI Expertise

• Hands-on experience with GenAI platforms such as

  ChatGPT, Copilot, Azure OpenAI, Hugging Face Transformers

  , etc.
• Design, develop, and deploy GenAI solutions for business use cases including

  content generation, summarization, automation, and intelligent assistants

  .
• Apply

  prompt engineering

  , model fine-tuning, and evaluation techniques to improve GenAI outputs.
• Integrate GenAI capabilities into enterprise applications and workflows.

2. Python & Data Science

• Develop and maintain AI/ML models using

  Python

  and libraries such as

  pandas, NumPy, scikit-learn, TensorFlow, PyTorch, LangChain

  .
• Perform

  data analysis, feature engineering, and model validation

  to support GenAI applications.
• Build

  data pipelines

  and automate data processing tasks for scalable AI solutions.
• Use Python for

  API integration

  , backend logic, and orchestration of GenAI workflows.

3. Cloud Technologies

• Deploy and manage GenAI and ML solutions on

  cloud platforms

  like

  Azure, AWS, or GCP

  .
• Utilize cloud-native services such as

  Azure Machine Learning, AWS SageMaker, GCP Vertex AI

  , and serverless functions.
• Ensure scalability, security, and compliance of cloud-based AI solutions.
• Implement CI/CD pipelines and containerization using

  Docker, Kubernetes

  for GenAI applications.

4. Business Analysis & AI Integration

• Translate business requirements into technical specifications for GenAI solutions.
• Conduct stakeholder workshops to identify opportunities for GenAI adoption.
• Align AI initiatives with business strategy and KPIs.
• Collaborate with cross-functional teams to ensure successful implementation and adoption.

5. Agile & Collaboration

• Work within Agile teams, contributing to sprint planning, backlog refinement, and iterative delivery.
• Partner with Product Owners, Developers, and Data Scientists to co-create GenAI features.
• Advocate for continuous improvement and Agile best practices in AI development.

6. Risk, Compliance & Ethics

• Ensure GenAI solutions adhere to

  data privacy, security, and ethical standards

  (e.g., GDPR, AI ethics).
• Collaborate with risk and compliance teams to mitigate potential issues.
• Maintain transparency and explainability in AI models and outputs.

7. Continuous Learning & Innovation

• Stay updated on the latest trends in

  GenAI, LLMs, Python frameworks, cloud AI services

  , and data science.
• Experiment with emerging tools and techniques to enhance solution capabilities.
• Share knowledge and mentor team members on GenAI best practices.