Phyton Developer (GenAI & Banking)
Job Responsibilities: Develop object-oriented applications using Python with expert-level software engineering practices.Work with Vector Databases for embedding storage and retrieval optimization.Design and implement GenAI lifecycle management and RAG pipelines (chunking, embedding, retrieval, re-ranking, summarization) for banking use cases.Evaluate and test embedding models and frameworks for performance, latency, memory, and cost optimization.Apply prompt engineering , hallucination mitigation, and grounding techniques for AI solutions.Build API-driven applications using FastAPI , API Gateway , and integrate with MongoDB , Redis, and front-end frameworks ( Angular/React ).Develop utilities, automation frameworks , and data pipelines to support AI/ML and GenAI initiatives.Create monitoring dashboards and automation scripts for system health and performance.Collaborate with DevOps teams using enterprise tools (Git/Bitbucket , Jenkins , SonarQube , Artifactory , Ansible ).Work with large cross-functional teams to deliver secure, compliant, and scalable solutions. Experience: 5+ years of experience in object-oriented programming using Python with strong software development skills.Deep experience in GenAI lifecycle management and RAG pipelines.Expertise in prompt engineering and AI safety techniques.Hands-on experience with MongoDB, Redis, Fast API, containerization (Docker/Kubernetes), and API integrations.Strong knowledge of model testing and evaluation frameworks for performance and reliability.Solid understanding of DevOps practices and enterprise tooling.Understanding of Kafka queue integration and configurations.Ability to deploy python code on to containers using OpenShift and docker etc.Ability to work effectively with large cross-functional teams in a regulated environment. Preferred Skills: Experience with cloud platforms (AWS, Azure) and serverless architectures.Knowledge of Agentic architectures and orchestration frameworks.Exposure to AI/ML use cases in regulatory relations or risk analytics.Explore Agentic architectures for orchestration and multi-step reasoning in AI workflows.