GenAI- Python Lead

Reyika; Dadri, Delhi, Bengaluru; On-site; INR 1,800,000 - 3,200,000; Full time

Job summary

Reyika is seeking an experienced GenAI developer to build and optimize AI-powered applications. You will craft end-to-end GenAI solutions, implement RAG and Modular RAG, and fine-tune LLMs for domain data. The role requires strong Python, cloud deployment on Azure/GCP/AWS, and front-end integration with modern JS tools.

You will collaborate across teams, mentor junior developers, and help scale GenAI platforms while ensuring robust data transformation and anti-hallucination practices.

Qualifications

• Deep expertise in Python for GenAI apps and automation tools.
• Productionization of GenAI applications beyond PoCs with scalable frameworks.
• LLM frameworks like Autogen, Crew.ai experience.
• Experience handling large-scale structured and unstructured data.
• Experience with API integration including REST and SOAP.
• Fine-tune SLMs for domain data and use cases.
• Prompt injection fallback and RCE tools like Pyrit and HAX toolkit.
• Anti-hallucination tools and validation (e.g., Bleu).
• Front-end tech knowledge: React, Streamlit, AG Grid, JavaScript.
• Cloud deployment with Azure, GCP, AWS.
• Advanced skills in RAG and Modular RAG.
• Data modernization and transformation for GenAI apps.

Responsibilities

• Application Development: Build GenAI applications from scratch using frameworks like Autogen, Crew.ai, etc.
• Python Programming: Develop high-quality Python code for GenAI solutions.
• Implement Retrieval-Augmented Generation (RAG) and Modular RAG architectures.
• Familiarity with Multi-Modal LLM Applications.
• Fine-tune SLMs for domain-specific data and use cases.
• Cloud Platform Expertise: Deploy and manage GenAI apps on Azure, GCP, AWS.
• Data Modernization and Transformation pipelines for GenAI apps.
• Front-End Integration: Build interfaces with React/Streamlit/JS.
• LLMOps: Fine-tuning techniques and LLM deployment operations.
• Mentorship: Guide junior developers and foster technical excellence.

Skills

Python programming

GenAI applications

RAG architectures

Modular RAG

LLM implementation

Data handling at scale

Mentorship

Tools

Autogen

Crew.ai

REST

SOAP

React

Streamlit

AG Grid

JavaScript

Azure

GCP

AWS

Pyrit

HAX toolkit

Bleu

Job description

Role & responsibilities

1. Application Development: Build GenAI applications from scratch using frameworks like Autogen, Crew.ai, etc.
2. Python Programming: Develop high-quality, efficient, and maintainable Python code for GenAI solutions.
3. Implement Retrieval-Augmented Generation (RAG) & Modular RAG architectures for enhanced model performance.
4. Familiarity with Multi-Modal LLM Applications.
5. Fine-tune SLM(Small Language Model) for domain specific data and use cases.
6. Cloud Platform Expertise: Leverage Azure, GCP, and AWS for deploying and managing GenAI applications.
7. Data Modernization and Transformation: Design and implement data modernization and transformation pipelines to support GenAI applications.
8. 8.Front-End Integration: Implement user interfaces using front-end technologies ensuring seamless integration with GenAI backends.
9. Fine-Tuning LLMs techniques & LLMOps Implementation.
10. Mentorship: Guide and mentor junior developers, fostering a culture of technical excellence and innovation.

Preferred candidate profile

1. Deep expertise in Python for building GenAI applications and automation tools.
2. Productionization of GenAI application beyond PoCs Using scale frameworks and tools.
3. LLM Frameworks: Proficiency in LLM frameworks like Autogen, Crew.ai.
4. experienced in implementing architectures for handling large-scale structured and unstructured data.
5. Experience with REST, SOAP, and other protocols for API integration.
6. Fine-tune SLM(Small Language Model) for domain specific data and use cases.
7. Prompt injection fallback and RCE tools such as Pyrit and HAX toolkit etc.
8. Anti-hallucination and anti-gibberish tools such as Bleu etc.
9. Front-End Technologies: Strong knowledge of React, Streamlit, AG Grid, and JavaScript for front-end development.
10. Cloud Platforms: Extensive experience with Azure, GCP, and AWS for deploying and managing GenAI applications.
11. Advanced skills in RAG and Modular RAG
12. Expertise in modernizing and transforming data for GenAI applications.