We are seeking a skilled Generative AI Developer to conceptualize, build, and deploy cutting-edge GenAI applications. This role involves developing robust solutions from the ground up, leveraging advanced AI frameworks and programming languages.

Ideal candidates will possess a strong understanding of large-scale data handling, multi-modal LLM applications, and front-end integration. A passion for innovation and responsible AI practices is essential for success in this dynamic position.

Architect and develop GenAI applications using frameworks like Autogen, Crew.ai, LangGraph, LlamaIndex, and LangChain.

Write efficient and maintainable Python code for complex GenAI solutions.

Design and implement scalable architectures for managing vast amounts of structured and unstructured data.

Work with multi-modal LLMs, including text, vision, and speech models.

Fine-tune Small Language Models (SLMs) for domain-specific applications and use cases.

Integrate user interfaces using front-end technologies such as React, Streamlit, and AG Grid.

Develop and optimize data modernization and transformation pipelines.

Apply fine-tuning techniques like PEFT, QLoRA, and LoRA to enhance LLM performance.

Implement and manage LLMOps pipelines for seamless deployment and monitoring.

Embed responsible AI principles throughout the development lifecycle.

Demonstrated expertise in Python programming for GenAI applications and automation.

Proven ability to productionize GenAI applications beyond proof-of-concept stages, utilizing scalable frameworks.

Proficiency with LLM frameworks including Autogen, Crew.ai, LangGraph, LlamaIndex, and LangChain.

Experience in designing and implementing large-scale data handling architectures.

Familiarity with multi-modal LLM applications and related tools.

Strong knowledge of front-end technologies like React, Streamlit, AG Grid, and JavaScript.

Extensive experience with at least two major cloud platforms (Azure, GCP, AWS) for GenAI deployment.

Mastery of fine-tuning techniques such as PEFT, QLoRA, and LoRA.

Solid understanding of LLMOps practices for deployment and management.

Expertise in implementing responsible AI principles and ensuring compliance.

Advanced skills in RAG and Modular RAG architectures.

Proficiency in OCR and document intelligence solutions.

Experience with API integration using REST, SOAP, and other protocols.

Ability to build automated data curation and preprocessing pipelines.

Excellent technical documentation skills and strong collaboration abilities.