Data Science with Python Developer

Data Science with Python Developer

What You Will Do: Key Responsibilities

• Design, Architect, and Deliver AI/ML and/or Generative AI solutions

  hand-in-hand with business representatives, always considering the business ROI.

• Create and maintain optimal data pipeline architecture

  for efficient data flow and processing.

• Identify Data intensive and AI/ML Use Cases

  for different existing Managed Service accounts.

• Prepare technical presentations, design documents, and demonstrations

  for customer presentations on Data strategy and AI/ML Solutions.

• Prepare design and solution documents

  for both classic AI/ML and Generative AI-based solutions.

• Assemble large, complex data sets

  that meet various functional and non-functional business requirements.

• Design solutions that will keep data separated and secure

  across national boundaries through multiple data centers and strategic customers/partners, adhering to international security standards, organization, and customer security requirements.

What You Will Bring: Required Qualifications

• Experience:

  9 - 17 years of Telecom/IT experience in a Data-related Role with AI/ML and Data Science experience.

• Python Development:

  At least 5 years of experience using Python for

  AI/ML and automation

  , with proficiency in commonly used data engineering and machine learning libraries (e.g.,

  pandas, numpy, scikit-learn

  ).

• Data Pipeline Development:

  Experience with data pipeline development in technologies like

  Elastic (ELK) Stack, Hadoop, Spark, Kafka

  , etc.

• Database Knowledge:

  Experience with

  Relational SQL and NoSQL databases

  , such as Postgres and Cassandra.

• MLOps Experience:

  Hands-on experience with

  MLOps

  , including

  Model deployment and monitoring

  .

• AI/ML Solutions:

  Proven experience in developing AI/ML Solutions for

  prediction, classification, and Natural Language Processing

  .

• Generative AI Technologies:

  Working knowledge of

  Generative AI technologies

  like Large Language Models (LLMs), Agentic AI architecture, RAG (Retrieval-Augmented Generation), and other developing technologies.

• Containerization:

  Hands-on experience with

  Kubernetes and containerization technologies (e.g., Docker)

  .

• Operating Systems & Scripting:

  Proficiency in

  Linux & Shell scripting

  knowledge.