Senior Data Scientist - ML, Python

Bengaluru, Karnataka

Job Summary

Job Description: AI Lead Engineer (Wealth Data Platform)

Key Responsibilities

Natural Language Democratisation: Develop and deploy Text-to-SQL and Text-to-Insight interfaces that allow non-technical Wealth Managers to interact with the conformed data layer using LLMs.

Ontology & Knowledge Graph Engineering: Design and implement a domain-specific Wealth Ontology. Graph databases (e.g., Neo4j or Snowflake Relational Graphs) need to be leveraged to map complex client relationships and financial hierarchies that standard SQL fails to capture.

Agentic Workflows: Build and orchestrate Autonomous Agents (using frameworks like LangGraph, ADK, CrewAI, or AutoGen) capable of executing multi-step financial reasoning such as automated portfolio rebalancing checks or proactive client insight generation.

Modern Data Alignment: Ensure all AI models are integrated into the SageMaker Unified Studio and adhere to the bank’s OBDQ standards to prevent "hallucinations" in regulated client reporting.

Productivity Tooling: Work with Analytics Engineers to embed LLM-based chatbots into front-line tools to reduce manual data gathering time for client-facing staff.

Technical Requirements

AI/ML Foundations: Deep expertise in LLM orchestration (RAG), Fine-tuning, and Prompt Engineering.

Graph Technology: Experience building Ontologies or using Graph-based RAG to improve the retrieval of structured/unstructured wealth data.

Data Stack: Proficiency in Python and SQL. Familiarity with Snowflake (Cortex), AWS SageMaker, and Kafka for real-time agent triggers.

Engineering Rigor: Experience with LLMOps (monitoring, evaluation, and versioning) within a highly regulated Banking (FCA/PRA) environment.

Key Responsibilities

Job Description: AI Lead Engineer (Wealth Data Platform)

Key Responsibilities

Natural Language Democratisation: Develop and deploy Text-to-SQL and Text-to-Insight interfaces that allow non-technical Wealth Managers to interact with the conformed data layer using LLMs.

Ontology & Knowledge Graph Engineering: Design and implement a domain-specific Wealth Ontology. Graph databases (e.g., Neo4j or Snowflake Relational Graphs) need to be leveraged to map complex client relationships and financial hierarchies that standard SQL fails to capture.

Agentic Workflows: Build and orchestrate Autonomous Agents (using frameworks like LangGraph, ADK, CrewAI, or AutoGen) capable of executing multi-step financial reasoning such as automated portfolio rebalancing checks or proactive client insight generation.

Modern Data Alignment: Ensure all AI models are integrated into the SageMaker Unified Studio and adhere to the bank’s OBDQ standards to prevent "hallucinations" in regulated client reporting.

Productivity Tooling: Work with Analytics Engineers to embed LLM-based chatbots into front-line tools to reduce manual data gathering time for client-facing staff.

Technical Requirements

AI/ML Foundations: Deep expertise in LLM orchestration (RAG), Fine-tuning, and Prompt Engineering.

Graph Technology: Experience building Ontologies or using Graph-based RAG to improve the retrieval of structured/unstructured wealth data.

Data Stack: Proficiency in Python and SQL. Familiarity with Snowflake (Cortex), AWS SageMaker, and Kafka for real-time agent triggers.

Engineering Rigor: Experience with LLMOps (monitoring, evaluation, and versioning) within a highly regulated Banking (FCA/PRA) environment.

Skill Requirements

1. - Strong Knowledge Of Machine Learning Principles And Algorithms Using Tensorflow And Pytorch.

2. - Proficient In Programming Languages Such As Python For Data Analysis And Model Development.

3. - Solid Understanding Of Sql For Data Manipulation And Querying Large Databases.

4. - In-Depth Experience With Data Analytics Techniques And Tools For Interpreting Complex Datasets.

5. - Excellent Collaboration And Communication Skills To Work With Diverse Teams And Stakeholders.

Other Requirements

1. Optional But Valuable Certifications: Certified Data Scientist (Cds), Tensorflow Developer Certificate, Or Professional Data Engineer Certification.

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