Gen AI Developer​/Lead

*** No Visa Transfer/c2c/Sponsorship available now or in the future, for this role**

We are seeking a highly skilled and innovative Senior AI/ML Engineer with strong expertise in Python, PySpark, Azure Machine Learning, Generative AI, and Full Stack Development to design and deliver advanced analytics and AI-driven solutions for global investment banking and brokerage operations.

The ideal candidate will combine deep technical expertise in machine learning, distributed computing, cloud-native AI platforms, and modern AI frameworks such as Lang Chain, Lang Graph, RAG, Agentic AI Frameworks, FastAPI, and Azure OpenAI Service
. This role requires close collaboration with business stakeholders to transform complex financial data into actionable insights that improve decision-making, reduce operational risk, and enhance operational efficiency.

• Design, develop, and deploy advanced machine learning models using Python and Py Spark to analyze large-scale financial datasets and generate actionable business insights.
• Build predictive, classification, clustering, anomaly detection, forecasting, and risk models supporting investment banking and brokerage functions.
• Perform rigorous model validation, back-testing, and experimentation using historical and simulated market data.
• Evaluate and implement appropriate statistical, machine learning, deep learning, and AI techniques based on business requirements and regulatory considerations.
• Optimize model performance through feature engineering, hyperparameter tuning, algorithm enhancements, and distributed computing techniques.

• Design and implement enterprise-grade Generative AI solutions using Azure OpenAI Service
  .
• Build and deploy Retrieval-Augmented Generation (RAG) applications leveraging vector databases and knowledge retrieval systems.
• Develop intelligent agent-based systems using Lang Chain, Lang Graph, and Agentic AI frameworks to automate business workflows and enhance decision support.
• Apply Natural Language Processing (NLP), Large Language Models (LLMs), document intelligence, and conversational AI to streamline surveillance, reporting, compliance, and advisory functions.
• Ensure safe, responsible, and governed adoption of Generative AI capabilities across the organization.

• Design and develop scalable backend services and APIs using FastAPI
  .
• Build microservices and AI application frameworks that integrate machine learning and GenAI capabilities into enterprise ecosystems.
• Develop reusable and maintainable software components following modern software engineering best practices.
• Implement API integrations, authentication mechanisms, monitoring, logging, and performance optimization strategies.

• Design and implement scalable data pipelines and feature engineering workflows using Azure Machine Learning and cloud-native services.
• Build reusable data products and machine learning components supporting multiple analytics and AI initiatives.
• Partner with Data Engineering teams to operationalize machine learning models and AI applications.
• Establish model monitoring, retraining strategies, experiment tracking, and lifecycle management processes.
• Ensure solutions are secure, reliable, scalable, and production-ready.

• Develop end-to-end ML and AI solutions using:
  • Azure Machine Learning
  • Azure OpenAI Service
  • Azure Data Lake
  • Azure Databricks
  • Azure Storage Services
  • Azure Dev Ops
• Manage model deployment, monitoring, governance, and operationalization on Azure platforms.
• Support enterprise-scale AI and analytics workloads while maintaining compliance and security standards.

• Collaborate with product owners, business analysts, operations teams, and technology stakeholders to define high-value data science initiatives.
• Translate complex investment banking and brokerage business challenges into measurable analytical solutions.
• Present recommendations and analytical findings to both technical and non-technical audiences.
• Drive adoption of AI and machine learning solutions through effective communication and stakeholder engagement.

• Promote responsible AI practices by evaluating model fairness, explainability, bias, security, and data quality.
• Document assumptions, risks, methodologies, and limitations in a transparent and accessible manner.
• Ensure adherence to regulatory requirements, model governance…