Summary
✨ AI‑Generated
A Python engineering role focused on building scalable applications, AI-powered workflows, APIs, and data systems. The position requires strong software development skills and experience with modern AI technologies.
Highlights
Role involving advanced AI application development, scalable backend systems, and collaboration across engineering and business teams.
Description
Key Responsibilities
Design, develop, and maintain scalable Python applications and microservices.Build and integrate AI/ML solutions, including Large Language Models (LLMs) and Generative AI applications.Develop REST APIs and backend services using FastAPI, Flask, or Django.Design and implement Retrieval-Augmented Generation (RAG) solutions and AI-assisted workflows.Work with vector databases and embedding models to support semantic search and knowledge management applications.Integrate AI services such as OpenAI, Azure OpenAI, Gemini, Claude, or similar platforms.Develop data pipelines for model training, inference, and AI-driven automation.Collaborate with Product Owners, Architects, Data Engineers, and Business Stakeholders to deliver enterprise-grade solutions.Optimize application performance, scalability, security, and reliability.Participate in Agile development processes, code reviews, testing, and CI/CD activities.
Roles & Responsibilities
Required Skills
8+ years of hands-on experience in Python development.Strong expertise in Python frameworks such as FastAPI, Flask, or Django.Experience with RESTful APIs, Microservices Architecture, and distributed systems.Good understanding of Machine Learning, NLP, and Generative AI concepts.Hands-on experience working with LLMs, Prompt Engineering, and AI model integration.Experience with LangChain, LangGraph, Semantic Kernel, CrewAI, AutoGen, or similar AI orchestration frameworks.Knowledge of RAG architecture, embeddings, and vector databases such as Pinecone, FAISS, Weaviate, or ChromaDB.Proficiency in SQL and database design principles.Experience with Git, CI/CD pipelines, Docker, and containerized environments.Strong problem-solving, analytical, and communication skills.
Preferred Skills
Experience with cloud platforms such as AWS, Azure, or GCP.Knowledge of MLOps, LLMOps, and model deployment practices.Experience with PyTorch, TensorFlow, Scikit-learn, Pandas, and NumPy.Familiarity with Kafka, Spark, Databricks, or Snowflake.Exposure to AI governance, Responsible AI, and model monitoring frameworks.Experience in Banking, Financial Services, Capital Markets, or other regulated industries.
