Job responsibilities :

Technical AI Leadership :

- Lead the charge for Data and Agentic AI architecture, Retrieval Augmented Generation (RAG) for text2sql, Prompt Engineering, MCP APIs and evaluating the AI solutions thoroughly for making the vast finance data sitting within the Securities Services data lake AI ready.

Understanding business needs and Quick Prototyping :

- Effectively Code and Prototype end-to-end software applications including analytics, dashboards, python applications using AI coding toolkits like github copilot.

Communication and Collaboration :

- Communicate and collaborate effectively to translate complex technical business problems into achievable impactful book of work.

Stakeholder Management :

- Engage with internal and external stakeholders, including business users, technology teams, and clients, to gather requirements and prioritize features.

Delivery & Execution :

- Oversee end-to-end product delivery, from concept to deployment, ensuring high-quality releases and continuous improvement.

Required qualifications, capabilities & skills :

- Bachelor's or Master's degree in Computer Science, Engineering, or related field.

- 5 - 7 years for Associate & 7+ for VP years of experience in Applied AI/ML, Data engineering, or related roles, with significant exposure to data lake technologies (e.g., Databricks, Hadoop, Spark, AWS, Azure) and AI/ML solutions on financial data.

- Real Hands-on-experience in designing, scaling and evaluating large scale industry AI projects like RAG pipelines for text2sql, Agentic State Architecture, MCP APIs creation.

- Rapid coding/prototyping skills in python using AI guided copilot programming.

- Proven track record of delivering complex, large-scale data solutions in a hands-on capacity.

- Deep understanding of data architecture, ETL processes, data quality frameworks, data lineage, and analytics platforms.

- Deep understanding of context engineering, RAG, fine-tuning AI models on structured and unstructured data.

- Excellent communication, stakeholder management, and leadership skills.

- Experience working in global, cross-functional teams.

Good-to-have :

- Real Hands-on-experience in finetuning LLM models for large scale industry projects.

- Traditional Machine Learning/Data Science background using models like xgboost, logistic regression for large scale industry projects.