Lead Consultant(Development)

Experience: Not Available to Not Available years

Location: Bengaluru, India

Skills: Python, SQL, LLM orchestration, Fine-tuning, Prompt Engineering, Graph Technology, Graph-based RAG, Snowflake, AWS SageMaker, Kafka, LLMOps, MLOps, Agentic AI frameworks, Knowledge graph, Neo4j

Job Summary
Job Description: AI Lead Engineer (Wealth Data Platform)

Key Responsibilities
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.

Skill Requirements
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) setting.
✅Mandatory YES/NO filters:
✅ Python + SQL (strong hands-on)
✅ LLM experience (RAG + Prompt Engineering)
✅ Built GenAI/LLM applications (real projects)
✅ PyTorch or TensorFlow
✅ Data engineering exposure
✅ Cloud (AWS/Snowflake)
✅Strong preference:
✅ Agentic AI frameworks
✅ Knowledge graph / Neo4j
✅ Kafka / real-time systems
✅ LLMOps / MLOps
✅ Banking / regulated domain
Other Requirements
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📌 Lead Consultant(Development) (Bengaluru)
🏢 HCL Technologies
📍 Bengaluru