Generative AI Engineer

Role – GEN AI Engineer

Location – Noida

Notice Period – Immediately- 30 days Days

Role – GEN AI Developer

Key Responsibilities:

1. Application Development: Build GenAI applications from scratch using frameworks like Autogen (applied or acquired), Crew.ai, 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.

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 Pylint,Pyrit etc
3. .LLM Frameworks: Proficiency in frameworks like Autogen, Crew.ai, 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