Python Developer

Python Developer

AI/ML Engineering

• Develop, train, and optimize ML models using PyTorch, TensorFlow, and Keras.
  
• Build end-to-end LLM and RAG pipelines using LangChain and LangGraph.
  
• Work with LLM APIs (OpenAI, Anthropic Claude, Azure OpenAI) and implement prompt engineering strategies.
  
• Utilize Hugging Face Transformers for model fine-tuning and deployment.
  
• Integrate embedding models for semantic search and retrieval systems.
  
• Work with transformer-based architectures (BERT, GPT, LLaMA, Mistral) for production use cases.
  
• Implement LLM evaluation frameworks (RAGAS, LangSmith) and performance optimization.
  

Backend & Systems Development

• Design and maintain Python microservices using Django and FastAPI with REST/GraphQL APIs.
  
• Implement real-time communication with Django Channels and FastAPI WebSockets.
  
• Design and optimize PostgreSQL databases with pgvector for embedding storage and similarity search.
  
• Integrate vector databases (Pinecone, Weaviate, FAISS, Milvus) for retrieval pipelines.
  
• Containerize AI services with Docker and deploy on Kubernetes (EKS/GKE/AKS).
  
• Configure AWS infrastructure (EC2, S3, RDS, SageMaker, Lambda, CloudWatch) for AI/ML workloads.
  
• Implement Redis caching and message brokers (RabbitMQ/Redis) with Celery for background tasks.
  

• Required Qualifications

• Bachelor's or Master's degree in Computer Science, AI/ML, or related field with 25 years of Python experience (Python 3.9+).
  
• Hands-on experience with LangChain and LangGraph for building LLM-powered workflows and RAG systems.
  
• Strong background in deep learning (PyTorch/TensorFlow), Hugging Face Transformers, and LLM APIs (OpenAI, Anthropic, Azure OpenAI).
  
• Proficiency with Django and FastAPI frameworks for building production APIs.
  
• Experience with PostgreSQL and pgvector for embedding storage and similarity search.
  
• Hands-on with MLOps (MLflow/W&B), Docker, Kubernetes, and AWS (EC2, S3, RDS, SageMaker).
  
• Skilled in Git workflows, automated testing (pytest), CI/CD, and AI security best practices.
  
• Excellent communication and analytical thinking.
  

Nice to Have

• Experience with vector databases (Pinecone, Weaviate, FAISS, Milvus).
  
• Knowledge of advanced LLM fine-tuning (LoRA, QLoRA, PEFT) and RLHF.
  
• Experience with model serving frameworks and distributed training.
  
• Familiarity with workflow orchestration tools (Airflow, Prefect, Dagster).
  
• Knowledge of quantization and model compression techniques.
  
• Experience with infrastructure as code (Terraform, CloudFormation).
  
• Familiarity with data versioning tools (DVC) and AutoML.
  
• Experience with Streamlit or Gradio for ML demos.
  
• Background in statistics, optimization, or applied mathematics.
  
• Contributions to AI/ML or LangChain/LangGraph open-source project

  Role & responsibilities