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.

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

KEY 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, modelling workflow, testing, and deployment.

REQUIRED EXPERIENCE :

• 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.

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.

IDEAL CANDIDATE

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.