Senior Python Backend Engineer

Job Overview:

Senior Python Backend Engineer

Key Responsibilities:

• Design, develop, and maintain scalable backend services and APIs in Python (FastAPI, Flask, Django).
• Build data ingestion, transformation, and feature engineering pipelines for AI/ML and GenAI.
• Implement microservices with reliability, observability, and maintainability.
• Optimize backend systems for performance, cost, and scalability in cloud environments.
• Work with data scientists to productionize ML/GenAI models (deployment, monitoring, lifecycle).
• Integrate LLMs, vector databases, embeddings, and RAG pipelines into services.
• Develop APIs and SDKs to expose AI/ML capabilities.
• Apply responsible AI practices: bias detection, security, compliance.
• Translate business needs into technical designs with architects and product teams.
• Mentor junior engineers, enforce best practices, and support CI/CD automation.
• Contribute to design discussions, architecture reviews, and sprint planning.

Key Skills:

• 6+ years of backend engineering experience with

  Python

  in enterprise or product environments.
• Strong expertise in

  FastAPI, Flask, Django, or similar frameworks

  .
• Proven experience working on

  AI/ML/GenAI projects

  (model deployment, serving, integration).
• Hands-on with

  data processing frameworks

  (Pandas, PySpark, Dask) and

  ML frameworks

  (PyTorch, TensorFlow, Hugging Face).
• Familiarity with

  vector databases

  (Pinecone, Weaviate, FAISS, Milvus) and

  orchestration tools

  (Airflow, Dagster, Prefect).
• Strong understanding of

  REST/gRPC APIs, microservices, containerization (Docker, Kubernetes), and CI/CD pipelines

  .
• Cloud experience with

  AWS, Azure, or GCP

  (serverless, Kubernetes, AI/ML services).
• Solid understanding of

  security, scalability, and performance tuning

  in backend systems.

Preferred Qualifications

• Experience with

  LLMOps/ MLOps

  (MLflow, Weights & Biases, LangChain/LangGraph).
• Exposure to

  multi-agent systems, advanced RAG, or AI governance

  patterns.
• Prior experience in

  data-intensive applications

  (streaming, big data pipelines).
• Open-source contributions or publications in AI/ML/GenAI.