Manager

Job Title: Python Developer – Data Science & GenAI Solutions

Experience:

Department:

Band

Job Summary

Python Developer

automation, data science, machine learning, and NLP

Generative AI (GenAI)

Retrieval-Augmented Generation (RAG)

Key Responsibilities

• Develop and maintain Python-based automation scripts and pipelines for data ingestion, transformation, and model deployment.
• Build, train, and deploy machine learning models for predictive analytics and classification/regression tasks.
• Perform text analytics and Natural Language Processing (NLP), including text preprocessing, named entity recognition (NER), sentiment analysis, and topic modeling.
• Design and implement Generative AI solutions, including RAG pipelines, using tools like LangChain, LlamaIndex, or similar frameworks.
• Collaborate with data scientists and DevOps engineers to deploy solutions using cloud-native technologies (AWS preferred).
• Integrate models into production systems and ensure continuous delivery using version control systems like GitHub.
• Document code, workflows, and modeling decisions for both technical and non-technical stakeholders.
  

Required Skills And Qualifications

• Strong proficiency in Python and related libraries (e.g., Pandas, NumPy, Scikit-learn, FastAPI/Flask).
• Experience with automation frameworks and scripting tools for ETL or system processes.
• Solid background in data science and ML model development, including model evaluation and optimization.
• Working knowledge of NLP libraries (e.g., SpaCy, NLTK, HuggingFace Transformers).
• Familiarity with GenAI technologies, including prompt engineering, fine-tuning, and RAG architecture.
• Hands-on experience with Git/GitHub for version control and collaboration.
• Understanding of cloud-native architecture and ability to work in cloud environments (AWS).
• Ability to write modular, reusable, and well-tested code.
  

Preferred Qualifications

• Exposure to MLOps practices including CI/CD for ML, model monitoring, and pipelines.
• Experience with vector databases (e.g., Pinecone, FAISS, Weaviate) for RAG-based solutions.
• Knowledge of REST APIs and microservices architecture.