Principal AI Engineer

Turing•New York, NY

1d•$200,000 - $245,000

About The Position

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.

Requirements

10–12+ years of professional, hands-on engineering experience, including 4–6+ years specializing in building and deploying AI/ML models and systems.
1+ years of experience in developing Generative AI (LLM) applications using techniques like prompt engineering, RAG, and/or agents.
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.
Expert proficiency in programming skills in Python, Langchain/Langgraph and SQL is a must.
Experience in driving the engineering team toward a technical roadmap.
Excellent communication skills to effectively collaborate with business SMEs.

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.
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.