Gen AI Engineer

Key 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.
• Explore Agentic architectures for orchestration and multi-step reasoning in AI workflows.

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Required Skills & Experience

• Primary Skill: Python
• Secondary Skills: Vector DB, Django, Flask, FastAPI, KAFKA, Containerization (OpenShift, Docker etc.)
• Knowledge in GenAI lifecycle management and RAG pipelines.
• Expertise in prompt engineering and AI safety techniques.
• Hands-on experience with FastAPI, containerization (Docker/Kubernetes), and API integrations.
• 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.

Salary Range: $70,000 to $80,000 per year