Generative AI Developer - LLM/Python

Responsibilities :

- Understand client business use cases and technical requirements; translate them into scalable AI architecture designs.

- Evaluate and propose suitable generative AI solutions using state-of-the-art models such as GPT, BERT, LLaMA, and others.

- Architect and develop enterprise-grade Generative AI systems using Azure AI and other cloud services (AWS, GCP, etc. ).

- Implement prompt engineering and Retrieval-Augmented Generation (RAG) techniques to enhance LLM performance.

- Design, develop, and fine-tune LLMs using frameworks such as Hugging Face Transformers, LangChain, or similar.

- Integrate AI solutions into enterprise platforms and APIs with a strong focus on security, scalability, and maintainability.

- Drive POCs to validate architecture decisions and new technologies.

- Collaborate with cross-functional teams to ensure high-quality delivery and alignment with non-functional requirements (NFRs).

- Apply CI/CD and MLOps best practices to streamline AI model deployment and monitoring.

- Lead architectural reviews, design documentations, and enforce design standards across AI projects.

- Troubleshoot and resolve technical issues through thorough root cause analysis.

- Stay up to date with advancements in Generative AI, LLMs, and cloud technologies.

Requirements :

- 7+ years of total experience in software development with strong exposure to AI/ML systems.

- Proven expertise in Generative AI and Transformer-based models (e. g., GPT, BERT, LLaMA).

- Strong hands-on experience with Azure AI services and other cloud platforms (AWS, GCP).

- Proficient in Python and major ML libraries/frameworks like PyTorch, TensorFlow.

- Experience in prompt engineering, RAG pipelines, and multi-agent AI systems.

- Deep understanding of LLM fine-tuning, deployment, and serving pipelines.

- Hands-on experience with tools like Hugging Face Transformers, LangChain, and knowledge of NLP and Knowledge Engineering.

- Solid grasp of MLOps, CI/CD for AI, and integrating models into production environments.

- Strong architectural thinking with an ability to define and enforce best practices related to scalability, extensibility, security, and performance.

- Excellent problem-solving skills and ability to articulate technical concepts clearly.