Senior AI / ML Engineer

We are looking for a Senior AI/ML Engineer who can build production-grade AI systems for financial services clients. The role is hands-on and suited to someone with strong Python, machine learning, LLM, RAG, agentic AI, and data engineering experience. This person does not need to be a pure banking quant but should be able to work closely with quant finance and financial services teams to build robust AI and analytics solutions.

Responsibilities:

• Build AI/ML and GenAI systems for financial services use cases. - Develop RAG pipelines, LLM agents, workflow automation tools, and model-driven applications.
• Design and deploy Python-based APIs, dashboards, data pipelines, and model services.
• Work with financial datasets including transactions, credit data, market data, documents, policies, reports, and unstructured data.
• Support use cases across risk management, credit, treasury, compliance, markets, corporate banking, and management reporting.
• Build prototypes quickly and then harden them into production-ready systems.
• Work with senior finance/quant leads to convert financial methodologies into working software.
• Guide junior developers and analysts on code quality, modeling workflow, testing, and deployment.

Requirements:

• Technical Skills: Python, pandas, NumPy, scikit-learn, PyTorch, TensorFlow, FastAPI, Flask, Streamlit, LangChain, LangGraph, LlamaIndex, vector databases, embeddings, RAG pipelines, SQL, structured databases, Azure, AWS, GCP, Docker, Kubernetes, Git, CI/CD, testing, logging, basic MLOps practices.
• 7-10 years of experience in AI/ML engineering, data science, software engineering, or analytics engineering.
• Strong hands-on Python experience.
• Experience building real AI/ML systems, not only notebooks.
• Experience with LLMs, RAG, agentic workflows, NLP, or document intelligence.
• Experience deploying models or applications using APIs, cloud platforms, containers, or production workflows.
• Strong problem-solving ability and willingness to work in financial services.
• Someone who can build practical AI systems quickly, understand messy business problems, and work with finance specialists to deliver tools that clients can actually use.

Financial Services Exposure:

• Experience in one or more of the following would be preferred:
• Banking, fintech, payments, insurance, asset management, consulting, or capital markets.
• Credit risk, fraud, KYC, treasury, trading, portfolio analytics, regulatory reporting, or financial document processing.
• Market data, transaction data, financial statements, loan books, or unstructured financial documents.