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 to 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.