Specialist Software Engineer - AI Engineer (Sherpa)

Société Générale · Artificial Intelligence · Commercial & Retail Banks · Python · 1 day ago
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Location
In office (Bengaluru)
Employment
Full-Time

Confirmed on the employer's own hiring board on Nov 5, 2026. First seen by Alion on Nov 3, 2026. Société Générale scores B on the Alion truth index.

Overview
Company
Impact
Profile match

Société Générale

Société Générale is a French universal bank headquartered in Paris, with its main offices at La Défense, that provides retail banking in France, international retail banking and financial services, and corporate and investment banking. Founded in 1864, it is one of Europe's largest banking groups, operating in dozens of countries, listed on Euronext Paris and owning brands such as SG, Boursorama and Ayvens. Its postings cover retail branch advisers and SME relationship managers, recovery and risk staff, front office application support, IT analysts, quantitative analysts and many internships and VIE assignments.
societegenerale.com · HQ: Paris, France · Investment Banking & M&A Advisory · Commercial & Retail Banks · 5000+ employees · Est. 1864
Tech stack of Société Générale

Responsibilities

Key Responsibilities

  • Participate in knowledge transfer and transition of the existing Knowledge Graph capability to CFT/DAR.
  • Maintain, refine, and enhance the existing Neo4j Graph DB.
  • Build and enrich knowledge graphs from functional documentation.
  • Apply AI/LLM techniques to extract relevant entities, relationships, and context from documentation.
  • Use Knowledge Graph information to identify and suggest potential data lineage linkages.
  • Support creation and improvement of macro-level data lineage.
  • Fine-tune the solution based on feedback from Data Lineage and functional teams.
  • Continuously improve the accuracy and relevance of relationships captured within the Knowledge Graph.

Profile required

Primary Skills

  • Neo4j / Graph Databases
  • Knowledge Graph development and engineering
  • Graph data modelling
  • Python
  • Generative AI / Large Language Models (LLMs)
  • Entity and relationship extraction
  • Prompt and context engineering
  • AI-assisted processing of functional/unstructured documentation
Secondary Skills
  • Understanding of data lineage concepts
  • Natural Language Processing (NLP)
  • Experience working with functional and technical documentation
  • Understanding of enterprise data and metadata concepts
  • Ability to translate functional information into structured entities and relationships
  • Experience collaborating with functional SMEs, Data Lineage teams, and AI teams
  • Troubleshooting and continuous improvement of AI-generated outputs

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