Computer Scientist - II

Adobe Adobe · Enterprise · Noida, India +1

This role focuses on designing, building, and deploying Agentic AI solutions and LLM-powered applications, including multi-agent systems and RAG. It involves fine-tuning models, building scalable backend services and APIs, and establishing evaluation and observability frameworks for production-grade AI systems within an enterprise context.

What you'd actually do

  1. Design and build Agentic AI solutions, including multi-agent systems that automate complex business and engineering workflows.
  2. Develop and deploy LLM-powered applications using modern techniques such as RAG, Agentic RAG, Vectorless and reasoning framework.
  3. Fine-tune and optimize models using techniques such as LoRA, GRPO, and other parameter-efficient training approaches.
  4. Build scalable backend services, APIs, and AI platform capabilities that support production-grade AI applications.
  5. Establish evaluation, observability, and governance frameworks to ensure reliable, secure, and responsible AI systems.

Skills

Required

  • Python
  • LLMs
  • Agentic workflows
  • multi-agent architectures
  • AI orchestration frameworks
  • model optimization
  • fine-tuning techniques (LoRA, GRPO, RLHF)
  • event-driven architectures
  • streaming systems
  • real-time data processing
  • modern data platforms
  • data products
  • data mesh ecosystems
  • cloud-native architectures
  • Kubernetes
  • MLOps/LLMOps
  • AI system observability

Nice to have

  • modern enterprise solutions
  • Vectorless and reasoning framework
  • parameter-efficient training approaches
  • scalable backend services
  • APIs
  • AI platform capabilities
  • telemetry and operational data
  • data engineering teams
  • system design

What the JD emphasized

  • 7+ years of experience building production-scale AI/ML systems
  • Hands-on experience with LLMs, Agentic workflows, multi-agent architectures, and AI orchestration frameworks.
  • Experience with model optimization and fine-tuning techniques such as LoRA, GRPO, RLHF, or related approaches.

Other signals

  • building production-grade AI applications
  • design and build Agentic AI solutions
  • develop and deploy LLM-powered applications