Overview
Skills
Job Details
Our esteemed client is looking for people with LLM experience to join us in solving business problems for our Fortune 500 customers. You will be a key member of the client GenAI delivery organization and part of a GenAI project. You will be required to work with a team of other engineers across different skill sets. In the past, the GenAI delivery organization has implemented industry leading multi-agent LLM systems, RAG systems, and Open Source LLM deployments for major enterprises.
Required skills
5+ years of professional experience in building Machine Learning models & systems
1+ years of hands-on experience in how LLMs work & Generative AI (LLM) techniques particularly prompt engineering, RAG, and agents.
Experience in driving the engineering team toward a technical roadmap.
Expert proficiency in programming skills in Python, Langchain/Langgraph and SQL is a must.
Understanding of Cloud services, including Azure, Google Cloud Platform, or AWS
Excellent communication skills to effectively collaborate with business SMEs
Roles & Responsibilities
Develop and optimize LLM-based solutions: Lead the design, training, fine-tuning, and deployment of large language models, leveraging techniques like prompt engineering, retrieval-augmented generation (RAG), and agent-based architectures.
Codebase ownership: Maintain high-quality, efficient code in Python (using frameworks like LangChain/LangGraph) and SQL, focusing on reusable components, scalability, and performance best practices.
Cloud integration: Aide in deployment of GenAI applications on cloud platforms (Azure, Google Cloud Platform, or 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 and knowledge-sharing to the engineering team, fostering best practices in machine learning and large language model development.
Continuous innovation: Stay abreast of the latest advancements in LLM research and generative AI, proposing and experimenting with emerging techniques to drive ongoing improvements in model performance.