Data Scientist

Role Overview

Data Scientist

Key Responsibilities

1. Generative AI & Model Development

• Develop, fine-tune, and evaluate

  Generative AI/LLM models

  (GPT, LLaMA, Claude, etc.).
• Implement prompt engineering, RAG pipelines, vector databases, and model optimization.
  
• Build NLP/vision models using frameworks like

  PyTorch

  , TensorFlow, Transformers, LangChain, etc.

2. MLOps & Deployment

• Design and maintain automated MLOps pipelines using tools like

  SageMaker, MLflow, Kubeflow, Docker, CI/CD, Airflow

  .
• Manage model versioning, monitoring, retraining, and performance optimization.
  
• Ensure scalable, secure, and production-ready ML deployments.
  

3. Python Engineering

• Build high-quality data science code using

  Python

  , NumPy, Pandas, PyTorch, Scikit-learn.
• Write modular, reusable, and optimized code for data pipelines and model serving.
  

4. AWS Integration

• Integrate ML solutions with

  AWS services

  :
• S3, Lambda, SageMaker, API Gateway, Step Functions, DynamoDB, CloudWatch.
  
• Architect and build data pipelines and model APIs on AWS.
  
• Optimize cloud infrastructure for cost, performance, and security.
  

5. Collaboration & Stakeholder Management

• Work cross-functionally with engineering, product, and data teams.
  
• Translate business problems into ML/AI solutions.
  
• Present insights and model results to leadership and clients.
  

Required Skills & Experience

• 710 years of experience as a

  Data Scientist / ML Engineer

  .
• Strong knowledge of

  Generative AI

  (LLMs, RAG, embeddings, transformers).
• Proficient in

  Python

  , PyTorch, and ML frameworks.
• Hands-on experience with

  MLOps

  and end-to-end pipelines.
• Strong exposure to

  AWS cloud architecture & integrations

  .
• Experience building scalable AI/ML systems in production.
  
• Excellent communication and problem-solving abilities.
  

Good to Have

• Exposure to

  Vector DBs

  : Pinecone, FAISS, Milvus.
• Experience with

  serverless architectures

  .
• Familiarity with

  API development

  (Flask/FastAPI).
• Knowledge of data engineering basics (Spark, Kafka).