Applied AI ML Associate Senior-data Engineer

JPMorgan Chase JPMorgan Chase · Banking · Bengaluru, Karnataka, India · Asset & Wealth Management

This role focuses on building and maintaining data pipelines and feature stores for AI/ML products, specifically agentic AI and machine learning use cases within the financial services domain. It involves data ingestion, transformation, quality checks, and structuring data for RAG and feature engineering, while adhering to regulatory standards.

What you'd actually do

  1. Builds and maintains scalable data pipelines and feature stores supporting the team's AI/ML products, from source systems through to production
  2. Implements data quality, validation, and monitoring so downstream models and applications can trust their inputs
  3. Structures and models data for retrieval-augmented generation, feature engineering, and analytics workloads
  4. Partners with AI engineers to productionise data flows for agentic AI and ML use cases
  5. Applies data access, privacy, and residency controls to firm-wide and cross-border regulatory standards

Skills

Required

  • 3+ years applied experience in software or data engineering
  • Proficiency in Python and SQL
  • Familiarity with modern software engineering practices (testing, code review, version control)
  • Hands-on experience building and maintaining data pipelines and ETL/ELT workflows
  • Exposure to distributed data processing and platforms such as Spark / Databricks
  • Working knowledge of cloud-native data services, containerisation, and CI/CD
  • Understanding of data modelling and warehousing concepts
  • Ability to communicate clearly with engineers, data scientists, and stakeholders
  • BSc in Computer Science, Data Engineering, or a related quantitative field

Nice to have

  • Industry-recognised data engineering certification (e.g., AWS Certified Data Engineer - Associate (DEA-C01), or similar)
  • Exposure to data foundations for ML / LLM workloads (feature stores, vector stores, RAG pipelines)
  • Exposure to financial services, particularly wealth, private banking, or asset management
  • Familiarity with data governance, lineage, and privacy tooling
  • Familiarity with JPM-internal data and AI/ML infrastructure for internal candidates

What the JD emphasized

  • agentic AI
  • machine learning products
  • retrieval-augmented generation
  • regulatory standards

Other signals

  • data pipelines
  • feature stores
  • agentic AI
  • machine learning products
  • data quality
  • retrieval-augmented generation