Sr. Gen AIML LeadDeveloper

Experteer Overview As a Senior AI/ML Engineer, you will design and deploy AI-driven analytics and GenAI solutions for global investment banking and brokerage operations. You collaborate with business stakeholders to transform financial data into actionable insights, improving decision-making and reducing risk. You will work across data engineering, MLOps, and cloud platforms to build scalable models and AI applications that enable governance and efficiency. This role blends advanced analytics with enterprise-grade GenAI capabilities, offering a meaningful impact at scale. Compensation / Benefits * Design, develop and deploy ML models on Python and PySpark for large-scale financial data * Build predictive, classification, clustering, anomaly detection, forecasting, and risk models * Conduct model validation, back-testing and experimentation with historical data * Evaluate ML techniques and ensure regulatory compliance in solutions * Optimize model performance via feature engineering and distributed computing * Design and deploy enterprise-grade GenAI solutions using Azure OpenAI Service * Develop RAG applications leveraging vector databases and knowledge retrieval * Create agent-based systems with LangChain, LangGraph, and Agentic AI frameworks * Apply NLP/LLMs for surveillance, reporting, compliance, and advisory functions * Ensure safe, governed adoption of Generative AI * Design scalable backend services and APIs with FastAPI * Develop AI components in microservices and enterprise ecosystems * Implement API integrations, authentication, monitoring and performance optimization * Create scalable data pipelines and feature engineering workflows with Azure ML * Partner with data engineering to operationalize models and AI apps * Establish model monitoring, retraining, experimentation tracking, and lifecycle management * Ensure security, reliability, scalability, and production-readiness * Collaborate with product owners, analysts, and operations to define data science initiatives * Translate financial challenges into measurable analytical solutions * Present findings to technical and non-technical audiences * Drive AI/ML adoption through stakeholder engagement * Promote responsible AI with fairness, explainability, bias and data quality considerations * Document methods, risks, and limitations clearly * Mentor junior data scientists and engineers * Foster innovation and technical excellence Tasks * 8+ years in Data Science, ML, or AI Engineering * Expert Python and PySpark for large-scale data processing * Experience with FastAPI, REST APIs, Microservices, OOP, software engineering best practices * Hands-on with LangChain, LangGraph, RAG architectures, Agentic AI frameworks, LLM development * Strong skills in Azure Machine Learning, Azure OpenAI Service, Azure Databricks, Azure Data Lake, MLOps/CI-CD * Experience deploying enterprise AI/ML solutions in cloud environments * Deep knowledge of supervised/unsupervised learning, deep learning, ensemble methods, NLP, time-series forecasting, anomaly detection, risk modeling * Domain experience in Investment Banking, Capital Markets, Brokerage Operations, trade surveillance, risk management * Excellent communication and stakeholder management; ability to explain tech to non-tech * Distributed/hybrid team collaboration * Preferred: vector databases (Pinecone, Azure AI Search, Weaviate, ChromaDB); Docker/Kubernetes; CI/CD; Responsible AI and governance; Azure certifications Key requirements * Medical/Dental/Vision/Life Insurance * Paid holidays and PTO * 401(k) plan and contributions * Disability (long/short-term) * Paid Parental Leave * Employee Stock Purchase Plan

of distributed computing * Design and deploy enterprise-grade GenAI solutions using Azure OpenAI Service * Develop RAG applications leveraging vector databases and knowledge retrieval * Create agent-based systems with LangChain, LangGraph, and Agentic AI frameworks * Apply NLP/LLMs for surveillance, reporting, compliance, and advisory functions * Ensure safe, governed adoption of Generative AI * Design scalable backend services and APIs with FastAPI * Develop AI components in microservices and enterprise ecosystems * Implement API integrations, authentication, monitoring and performance optimization * Create scalable data pipelines and feature engineering workflows with Azure ML * Partner with data engineering to operationalize models and AI apps * Establish model monitoring, retraining, experimentation tracking, and lifecycle management * Ensure security, reliability, scalability, and production-readiness * Collaborate with product owners, analysts, and operations to define data aaaaas _ initiatives * Translate financial challenges into measurable analytical solutions * Present findings to technical and non-technical audiences * Drive AI/ML adoption through stakeholder engagement * Promote responsible AI with fairness, explainability, bias and data quality considerations * Document methods, risks, and limitations clearly * Mentor junior data scientists and engineers * Foster innovation and technical excellence Tasks * 8+ years in Data Science, ML, or AI Engineering * Expert Python and PySpark for large-scale data processing * Experience with FastAPI, REST APIs, Microservices, OOP, software engineering best practices * Hands-on with LangChain, LangGraph, RAG architectures, Agentic AI frameworks, LLM development * Strong skills in Azure Machine Learning, Azure OpenAI Service, Azure Databricks, Azure Data Lake, MLOps/CI-CD * Experience deploying enterprise AI/ML solutions in cloud environments * Deep knowledge of supervised/unsupervised learning, deep learning, aaaaaa AI methods, NLP, time-series forecasting, anomaly detection, risk modeling * Domain experience in Investment Banking, Capital Markets, Brokerage Operations, trade surveillance, risk management * Excellent communication and stakeholder management; ability to explain tech to non-tech * Distributed/hybrid team collaboration * Preferred: vector databases (Pinecone, Azure AI Search, Weaviate, ChromaDB); Docker/Kubernetes; CI/CD; Responsible AI and governance; Azure certifications Key requirements * Medical/Dental/Vision/Life Insurance * Paid holidays and PTO * 401(k) plan and contributions * Disability (long/short-term) * Paid Parental Leave * Employee Stock Purchase Plan