Generative AI Engineer

About the role:

Turing 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 Turing GenAI delivery organization and part of a GenAI project. You will be required to work with a team of other Turing engineers across different skill sets. In the past, the Turing GenAI delivery organization has implemented industry leading multi-agent LLM systems, RAG systems, and Open Source LLM deployments for major enterprises.

Required skills

10+ 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.

Expert proficiency in programming skills in Python, Langchain/Langgraph and SQL is a must.

Expert in architecting GenAI applications/systems using various frameworks & cloud services

Good proficiency in using various cloud services from Azure, GCP, or AWS for building the GenAI applications

Experience in driving the engineering team toward a technical roadmap.

Excellent communication skills to effectively collaborate with business SMEs

Roles & Responsibilities

Build the technical roadmap given a business requirement and own the delivery of the same

Lead the engineering team toward a technical roadmap and ensure timely execution of the roadmap to achieve customer satisfaction.

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, GCP, 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.