AI Engineer with Python

Job Summary

We are looking for an experienced AI Engineer with strong Python expertise to join our banking technology team. The ideal candidate will design, develop, and deploy AI/ML solutions for banking and financial-services use cases, with a strong focus on Generative AI, Large Language Models (LLMs), machine learning, NLP, and production-grade Python applications.

The candidate will work closely with data scientists, software engineers, product teams, and banking stakeholders to build secure, scalable, and compliant AI solutions.

Key Responsibilities

• Design, develop, and deploy AI/ML solutions using Python for banking and financial-services use cases.
• Develop applications using Generative AI, LLMs, NLP, RAG, prompt engineering, and AI agents.
• Build and maintain production-quality Python services, APIs, and AI pipelines.
• Develop Retrieval-Augmented Generation (RAG) solutions using vector databases and enterprise knowledge repositories.
• Integrate LLMs and AI services with existing banking applications and data platforms.
• Develop and optimize machine-learning models for use cases such as:
• Fraud detection
• Credit risk and underwriting
• Customer segmentation
• Transaction monitoring
• Document intelligence
• Customer service automation
• Financial document analysis
• Regulatory/compliance use cases
• Implement model evaluation, monitoring, versioning, and continuous improvement.
• Work with structured and unstructured banking data while maintaining high standards for security, privacy, governance, and regulatory compliance.
• Build REST APIs and microservices using frameworks such as FastAPI or Flask.
• Collaborate with DevOps/MLOps teams to deploy AI solutions on cloud platforms.
• Optimize AI applications for performance, scalability, reliability, and cost.
• Participate in code reviews, architecture discussions, testing, and technical documentation.

Required Skills

Programming

• Strong hands-on experience with Python
• Object-oriented programming and software engineering principles
• REST APIs and microservices
• FastAPI / Flask
• Git and CI/CD

AI / Machine Learning

• Machine learning fundamentals and model development
• NLP and deep learning
• Generative AI and LLMs
• Prompt engineering
• RAG architecture
• Embeddings and vector search
• Experience with LLM frameworks such as LangChain, LlamaIndex, or similar
• Familiarity with model evaluation and observability

Cloud & Data

• Experience with AWS, Azure, or Google Cloud Platform
• SQL and relational databases
• NoSQL databases
• Experience with vector databases such as Pinecone, FAISS, Weaviate, Milvus, or Azure AI Search
• Docker and Kubernetes is a plus