Wcr- Quantitative Research - Associate

JPMorgan Chase JPMorgan Chase · Banking · Mumbai, Maharashtra, India · Commercial & Investment Bank

This role focuses on building and deploying AI/ML/LLM/GenAI solutions, with a strong emphasis on fine-tuning generative models for specific use cases, building AI agents, and implementing RAG-based solutions. The role also involves developing data pipelines, evaluating models, and communicating results.

What you'd actually do

  1. Design, develop, and deploy state-of-the-art AI/ML/LLM/GenAI solutions to meet business objectives.
  2. Develop and maintain automated pipelines for data, ensuring scalability, reliability, and efficiency.
  3. Implement optimization strategies to fine-tune generative models for domain specific use cases, ensuring high-quality outputs in summarization and text generation.
  4. Conduct thorough evaluations of generative models iterate on model architectures and implement improvements to enhance overall performance in AI/ML applications.
  5. Implement monitoring mechanisms to track AI/ML solutions performance in real-time and ensure model reliability.

Skills

Required

  • Python for model development, experimentation, and integration with LLM APIs
  • Identify and address AI/ML/LLM/GenAI challenges, implement optimizations, and fine-tune models
  • Cloud platforms (AWS)
  • Containerization technologies (Docker and Kubernetes)
  • Microservices design, implementation, and performance optimization
  • Building AI Agents (e.g., LangChain, LangGraph)
  • Integration of tools
  • RAG-based solutions
  • Knowledge Graphs (e.g., neo4J)
  • Developing end-to-end data workflows and ETL pipelines using Python and Databricks
  • Data modeling, normalization, and database design principles
  • Developing, debugging, and maintaining code in a large corporate environment
  • Gather, analyze, and synthesize large, diverse data sets
  • Develop visualizations and reports
  • Collaboration skills

Nice to have

  • Databricks

What the JD emphasized

  • state-of-the-art AI/ML/LLM/GenAI solutions
  • fine-tune generative models
  • AI/ML applications
  • AI/ML solutions performance
  • AI/ML/LLM/GenAI research
  • AI/ML/LLM/GenAI challenges
  • AI/ML applications
  • AI Agents
  • RAG-based solutions

Other signals

  • deploy state-of-the-art AI/ML/LLM/GenAI solutions
  • implement optimization strategies to fine-tune generative models
  • Proficient in building AI Agents (e.g., LangChain, LangGraph), integration of tools, and RAG-based solutions