Pwc Hiring Drive 2024 For Data Engineer – Gen AI – Associate 2 | Location Bangalore

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Pwc Hiring Drive 2024: Pwc Hiring Drive 2024 for its Data Engineer – Gen AI – Associate 2. PwC Drive organizes off-campus drives for candidates in any batch. To apply, candidates should have a Bachelor’s degree in Computer Science, Data Science, or a related field. if you are interested, please apply as soon as possible.

Company Name: Pwc

Role: Data Engineer – Gen AI – Associate 2

Location: Bangalore

Experience: 1-3 years

Qualification: Bachelor’s degree in Computer Science, Data Science, or a related field.

Batch: Any

Job Type: Full Time

Salary: 6.0 To 8.0 LPA (Expected)

Pwc Hiring Drive 2024

Job Description:

  • As an Associate, you’ll work as part of a team of problem solvers, helping to solve complex business issues from strategy to execution.
  • PwC Professional skills and responsibilities for this management level include but are not limited to:
  • Invite and give in the moment feedback in a constructive manner.
  • Share and collaborate effectively with others.
  • Identify and make suggestions for improvements when problems and/or opportunities arise.
  • Handle, manipulate and analyse data and information responsibly.
  • Follow risk management and compliance procedures.
  • Keep up-to-date with developments in area of specialism.
  • Communicate confidently in a clear, concise and articulate manner – verbally and in the materials I produce.
  • Build and maintain an internal and external network.
  • Seek opportunities to learn about how PwC works as a global network of firms.
  • Uphold the firm’s code of ethics and business conduct.


  • Bachelor’s degree in Computer Science, Data Science, or a related field.
  • 1-3 years of relevant experience, ideally with some exposure to GenAI projects.
  • Basic programming skills in Python.
  • Familiarity with data processing frameworks like Apache Spark or similar.
  • Understanding of SQL and basic database management systems.
  • Some knowledge of event-driven architectures and real-time data processing.
  • Exposure to containerization technologies like Kubernetes and Docker.
  • Awareness of data lakes and basic data lake management principles.
  • Some familiarity with LLM frameworks such as Langchain and Semantic Kernel.
  • Experience with cloud computing platforms such as Azure or AWS is a plus.
  • Strong analytical and problem-solving skills.
  • Good communication and collaboration abilities.
  • Ability to work in a fast-paced and dynamic environment.


  • Assist in the design, development, and maintenance of data pipelines and ETL processes for GenAI projects.
  • Work closely with data scientists and software engineers to support the implementation of machine learning models and algorithms.
  • Help optimize data infrastructure and storage solutions to ensure efficient data processing.
  • Gain experience with event-driven architectures to enable real-time data processing and analysis.
  • Learn and assist with containerization technologies like Kubernetes and Docker for deployment and scalability.
  • Support the development and maintenance of data lakes for managing large volumes of structured and unstructured data.
  • Contribute to the integration of LLM frameworks (such as Langchain and Semantic Kernel) for advanced language processing and analysis.
  • Collaborate with cross-functional teams to support the design and implementation of solution architectures for GenAI projects.
  • Utilize cloud computing platforms such as Azure or AWS under supervision for data processing, storage, and deployment.
  • Participate in monitoring and troubleshooting of data pipelines and systems to ensure smooth operations.
  • Stay informed about the latest advancements in GenAI technologies and assist in recommending innovative solutions to enhance data engineering processes.
  • Engage with cross-functional teams to help understand business requirements and contribute to translating them into technical solutions.
  • Assist in documenting data engineering processes, methodologies, and best practices.

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