Python Developer - AI/ML
Role details
Job location
Tech stack
Job description
- 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 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 and API Gateway; integrate with MongoDB, Redis, and front-end frameworks like Angular or 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 such as Git/Bitbucket, Jenkins, SonarQube, Artifactory, and Ansible.
- Work with large cross-functional teams to deliver secure, compliant, and scalable solutions.
- Explore Agentic architectures for orchestration and multi-step reasoning in AI workflows.
Requirements
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.
Technical Skills: The primary skill required is Python. Secondary skills include Vector DB, Django, Flask, FastAPI, Kafka, and containerization (OpenShift, Docker). Hands-on experience with MongoDB, Redis, and API integrations is necessary. Strong knowledge of model testing and evaluation frameworks, along with a solid understanding of DevOps practices and enterprise tooling is also required. Candidates must have an understanding of Kafka queue integration and the ability to deploy Python code onto containers.
Preferred Qualifications
- 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.