Sr LLM Engineer

As a Sr LLM Engineer at Turing, you will lead the design, training, fine-tuning, and deployment of large language models (LLMs). Your responsibilities include optimizing LLM-based solutions, maintaining high-quality code, deploying GenAI applications on cloud platforms, and collaborating with product owners and data scientists. Further, you’ll be in a mentoring role, guiding the engineering team while fostering continuous innovation in generative AI.

To apply for the Sr LLM Engineer role at Turing, candidates should have over 7 years of professional experience building machine learning models and systems, with at least 1 year of hands-on experience in LLM and Generative AI technologies. Proficiency in Python and SQL is essential, as well as familiarity with cloud services like AWS, Azure, or GCP. Good communication skills to collaborate effectively are also a must.

At Turing, a Sr LLM Engineer will engage in groundbreaking projects involving industry-leading multi-agent LLM systems, Open Source LLM deployments, and RAG systems for major enterprises. Your input in developing innovative AI solutions will help enhance the performance of LLMs for Fortune 500 clients, making a real-world impact with your technical expertise.

Yes, the Sr LLM Engineer position at Turing is fully remote, allowing you the flexibility to work from anywhere in India. Our remote work culture promotes collaboration and support among team members, giving you the opportunity to interact with top talent from around the globe while working on advanced technology solutions.

Turing fosters an amazing work culture that prioritizes collaboration, flexibility, and talent. As a Sr LLM Engineer, you'll be surrounded by colleagues from prestigious tech companies, receive competitive compensation, and benefit from a work environment that encourages growth and diversity. Plus, with full-time remote opportunities, you'll have the freedom to work in a way that suits you best.

Highlight your practical experience with LLMs, including specific technologies you’ve used and projects you’ve worked on. Discuss how you applied techniques like prompt engineering and RAG, focusing on the results you achieved.

Demonstrate your understanding of RAG by describing how it combines LLMs with external data retrieval. Share examples of scenarios where RAG improves response accuracy and relevance in generated content.

Discuss specific practices such as code reviews, unit testing, and documentation that you apply to maintain high-quality code. Emphasize reusable components and performance best practices.

Mention your experience with cloud services like AWS, Azure, or GCP. Provide examples of how you utilized these platforms for deploying AI models, and any CI/CD practices you implemented.

Share how you keep updated, whether through attending conferences, reading research papers, participating in online communities, or any other means of continuous learning related to AI advancements.

Detail a specific project, outlining the objectives, challenges you faced, and the approach you took to overcome them. Highlight the outcomes and any lessons learned.

Explain how you communicate technical details to non-technical stakeholders, share project requirements, and ensure everyone is aligned on goals. Mention the importance of active listening and adaptability.

Identify tools you have experience with, such as LangChain or LangGraph, and justify your choice based on flexibility, efficiency, and how they enhance your LLM projects.

Provide examples of times you coached team members or led knowledge-sharing sessions. Emphasize the importance of fostering best practices and supporting others in their professional growth.

Discuss current challenges such as model biases, data quality, or computational limits. Offer insights on possible solutions and the direction you think the field should take moving forward.