Computer Scientist ( Python )

Adobe Adobe · Enterprise · Bangalore, India

Senior Software Developer with Python and Generative AI expertise to lead the architecture and deployment of production-grade AI systems, focusing on LLMs, RAG, and Agentic workflows. Responsibilities include system architecture, GenAI implementation, model optimization, API development, data/MLOps, and technical leadership.

What you'd actually do

  1. System Architecture & Development: Lead the development of scalable backend systems and robust architectures for platforms powered by artificial intelligence, ensuring high availability and security.
  2. GenAI Implementation: Build and deploy production-scale Agentic AI workflows and multi-model RAG pipelines using frameworks like LangChain or LlamaIndex.
  3. Model Optimization: Fine-tune LLMs using techniques like LoRA or QLoRA and perform prompt engineering to improve model accuracy and efficiency.
  4. API Development: Develop high-performance RESTful or GraphQL APIs (typically using FastAPI or Flask) to integrate AI models with enterprise applications.
  5. Data & MLOps: Implement data ingestion and preprocessing mechanisms, while coordinating LLMOps practices such as model versioning, monitoring, and CI/CD for AI services.

Skills

Required

  • Python
  • Generative AI
  • Large Language Models (LLMs)
  • Retrieval-Augmented Generation (RAG)
  • Agentic workflows
  • LangChain
  • LlamaIndex
  • LoRA
  • QLoRA
  • prompt engineering
  • FastAPI
  • Flask
  • Pandas
  • NumPy
  • SQL
  • NoSQL
  • Docker
  • Kubernetes
  • CI/CD
  • Hugging Face
  • PyTorch
  • TensorFlow
  • LangGraph
  • CrewAI
  • Pinecone
  • Weaviate
  • FAISS
  • AWS
  • Azure
  • GCP

Nice to have

  • asynchronous programming (asyncio)
  • OOP
  • build patterns
  • vector search
  • vector storage
  • Jenkins
  • GitHub Actions
  • 7+ years in software development
  • 1-2 years specifically passionate about Generative AI or LLM integration
  • Bachelor's degree or equivalent experience
  • Master's degree or equivalent experience in Computer Science, Engineering, or a related STEM field

What the JD emphasized

  • production-grade AI systems
  • Agentic AI workflows
  • multi-model RAG pipelines
  • LLMOps practices

Other signals

  • production-grade AI systems
  • LLMs
  • RAG
  • Agentic workflows
  • LangChain
  • LlamaIndex
  • fine-tune LLMs
  • LLMOps