- Develop and Optimize LLM Solutions : Lead the design, training, fine-tuning, and deployment of large language models using techniques such as prompt engineering, retrieval-augmented generation (RAG), and agent-based architectures.

- Codebase Ownership : Maintain high-quality, efficient Python and SQL code, leveraging frameworks like LangChain/LangGraph, focusing on reusable components, scalability, and performance best practices.

- Cloud Integration : Assist in deploying Generative AI applications on cloud platforms (Azure, GCP, AWS), optimizing resource usage and ensuring robust CI/CD processes.

- Cross-Functional Collaboration : Work closely with product owners, data scientists, and business SMEs to define project requirements, translate technical details, and deliver impactful AI products.

- Mentoring and Guidance : Provide technical leadership, knowledge sharing, and guidance to engineering teams on best practices in machine learning and large language model development.

- Continuous Innovation : Stay current with LLM research and generative AI advancements, proposing and experimenting with emerging techniques to drive improvements in model performance.

Requirements :

- Experience : 5+ years in building machine learning models and systems.

- LLM Expertise : 1+ years hands-on experience with large language models and generative AI, including prompt engineering, RAG, and agents.

- Leadership : Experience guiding engineering teams toward a technical roadmap.

- Technical Skills : Expert proficiency in Python, LangChain/LangGraph, and SQL.

- Cloud Knowledge : Understanding of Azure, GCP, or AWS cloud services.

- Communication : Strong ability to collaborate with business SMEs and translate complex technical concepts.

Ideal Candidate : A technically strong, innovative LLM engineer with a track record of delivering scalable AI solutions, mentoring teams, and staying at the forefront of generative AI technologies.