Gen Ai developer

Location Noida

Notice Period Immediately- 30 days Days

Role GEN AI Developer

Mandatory skills

1. Python Programming

2. LLM Framework

3. Large-Scale Data Handling & Architecture:

4. Multi-Modal LLM Applications

5. Fine-tune SLM(

6. Front end techlogies such as React/Java script

7. Cloud platform

8. RAG and Moduclar

9. API Integration

Key Responsibilities:

1. Application Development: Build GenAI applications from scratch using frameworks like Autogen (applied or acquired) LangGraph LlamaIndex and LangChain.

2. Python Programming: Develop high-quality efficient and maintainable Python code for GenAI solutions.

3. Large-Scale Data Handling & Architecture: Design and implement architectures for handling large-scale structured and unstructured data.

4. Multi-Modal LLM Applications: Familiarity with text chat completion vision and speech models.

5. Fine-tune SLM(Small Language Model) for domain specific data and use cases.

6. Front-End Integration: Implement user interfaces using front-end technologies like React Streamlit and AG Grid ensuring seamless integration with GenAI backends.

7. Data Modernization and Transformation: Design and implement data modernization and transformation pipelines to support GenAI applications.

8. Fine-Tuning LLMs: Apply fine-tuning techniques such as PEFT QLoRA and LoRA to optimize LLMs for specific use cases.

9. LLMOps Implementation: Set up and manage LLMOps pipelines for continuous integration deployment and monitoring.

10. Responsible AI Practices: Ensure ethical AI practices are embedded in the development process.

11. innovation.

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Required Skills :

1. Python Programming: Deep expertise in Python for building GenAI applications and automation tools.

2. Productionization of GenAI application beyond PoCs Using scale frameworks and tools such as PylintPyrit etc.

3. LLM Frameworks: Proficiency in frameworks like Autogen LangGraph LlamaIndex and LangChain.

4. Large-Scale Data Handling & Architecture: Design and implement architectures for handling large-scale structured and unstructured data.

5. Multi-Modal LLM Applications: Familiarity with text chat completion vision and speech models.

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. (any two cloud exp.)

11. Fine-Tuning Techniques: Mastery of PEFT QLoRA LoRA and other fine-tuning methods. (any one is fine)

12. LLMOps: Strong knowledge of LLMOps practices for model deployment monitoring and management.

13. Responsible AI: Expertise in implementing ethical AI practices and ensuring compliance with regulations.

14. RAG and Modular RAG: Advanced skills in Retrieval-Augmented Generation and Modular RAG architectures.

15. Data Modernization: Expertise in modernizing and transforming data for GenAI applications.

16. OCR and Document Intelligence: Proficiency in OCR and document intelligence using cloud-based tools.

17. API Integration: Experience with REST SOAP and other protocols for API integration.

18. Data Curation: Expertise in building automated data curation and preprocessing pipelines.

19. Technical Documentation: Ability to create clear and comprehensive technical documentation.

20. Collaboration and Communication: Strong collaboration and communication skills to work effectively with cross-functional teams.