Computer Scientist 2 ( Python -genai )

Adobe Adobe · Enterprise · Noida, India

Lead Engineer role focused on designing, developing, and deploying scalable GenAI solutions, including agents, applications, and microservices. Involves building data workflows, managing API infrastructure, implementing RAG patterns, and optimizing AI performance. Requires strong Python, cloud, and AI/ML infrastructure knowledge.

What you'd actually do

  1. Design & Development of scalable GenAI agents, applications, and microservices
  2. Building complex and scalable data workflows
  3. Infrastructure & Deployment: Develop and manage large-scale API infrastructure along with CI/CD pipelines. Use containerization technologies like Docker and Kubernetes and cloud services such as AWS, GCP, and Azure
  4. RAG & Optimization: Implement solutions using RAG patterns, timely engineering, and model fine-tuning techniques to optimize AI performance and cost.
  5. Quality & Mentorship: Enforce code quality, conduct comprehensive code/build reviews, and provide technical leadership and mentorship to junior team members.

Skills

Required

  • Python
  • LangChain
  • Transformers
  • AWS
  • GCP
  • Azure
  • Docker
  • Kubernetes
  • CI/CD
  • RAG
  • model fine-tuning
  • vector databases
  • agentic workflows
  • SQL
  • Apache Spark
  • Databricks

Nice to have

  • GenAI models
  • LLMs
  • data engineering

What the JD emphasized

  • 8+ years of professional software development experience
  • around 2 years of experience with GenAI techniques or related concepts
  • Extensive knowledge in one or more programming languages, chiefly Python, and associated libraries (e.g., LangChain, Transformers)
  • Practical experience working with cloud platforms (AWS, GCP, or Azure), containerization tools (Docker, Kubernetes), and continuous integration and delivery pipelines.
  • Strong understanding of AI/ML infrastructure, model deployment/evaluation, vector databases, and AI-specific concepts like agentic workflows

Other signals

  • GenAI agents
  • LLMs
  • RAG
  • agentic systems
  • model fine-tuning
  • vector databases