Senior Python Application Developer

17 hours ago

Chennai, Tamil Nadu, India beBeePython Full time ₹ 1,50,00,000 - ₹ 2,00,00,000

Lead Python Architect Position

  • Oversee the design and implementation of Python-based applications integrated with GenAI capabilities.
  • Translate business requirements into technical solutions, ensuring modularity, scalability, and maintainability.
  • Mentor teams in implementing architecture blueprints for backend, infrastructure, and MLOps systems.
  • Evaluate tools and frameworks to recommend optimal solutions for LLM providers, vector databases, and cloud-based ML services.
  • Ensure security, scalability, and performance of deployed systems.
  • Stay current with advancements in GenAI and incorporate relevant capabilities into solution design.

Required Skills and Qualifications

  • 10+ years of experience in software engineering, with a minimum of 4 years in a technical leadership or architecture role.
  • Proven track record in building robust Python-based backend systems using frameworks such as FastAPI, Flask, or Django.
  • Expertise in microservices architecture, distributed system design, and integration patterns.
  • Strong familiarity with cloud platforms such as AWS, Azure, or GCP, and practical knowledge of AI/ML services like SageMaker, Vertex AI, or Azure ML.
  • Architectural experience with vector databases (e.g., Pinecone, FAISS, Weaviate, Qdrant) for semantic search and RAG pipelines.
  • Understanding of DevOps practices, including Docker, Kubernetes, and infrastructure as code.
  • Knowledge of CI/CD processes, system security, and observability/monitoring frameworks.

GenAI & AI/ML Architecture Expertise

  • Sound understanding of GenAI system components, including LLM lifecycles, embedding generation, prompt orchestration, and RAG architectures.
  • Ability to architect complete GenAI workflows from context ingestion and enrichment to inference handling and post-processing.
  • Familiarity with API orchestration, agent-based design patterns, and semantic search strategies.
  • Solid understanding of AI/ML engineering principles and pipeline design is essential to ensure the architecture supports model and data requirements.

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