About the Role:
Turing is hiring a Principal GenAI Engineer to lead enterprise-scale AI implementations for Fortune 500 clients. This role focuses on designing and building scalable, production-grade GenAI systems powered by LLMs, Retrieval-Augmented Generation (RAG), and agent-based architectures.
You will work at the intersection of backend/fullstack engineering and applied AI, building reliable, high-performance systems that integrate LLM capabilities into real-world applications.
Required skills:
- 10+ years of professional experience in software engineers and building applications/systems
- 2+ years of hands-on experience in how LLMs work & Generative AI (LLM) techniques particularly multi-agent systems.
- Expert proficiency in programming skills in Python, Langgraph and SQL is a must.
- Expert in architecting GenAI applications/systems using various frameworks & cloud services
- Expert proficiency in using AI tools like claude code, codex, cursor, windsurf and the likes.
- Expert proficiency in AI observability & evaluation tools like Langsmith, Langfuse or similar
- 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:
- Solutioning & Lead
- 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.
- Design robust multi-agent architectures including supervisor-router patterns with dynamic sub-agent routing and stopping conditions
- Mentoring and guidance: Provide technical leadership and knowledge-sharing to the engineering team, fostering best practices in machine learning and large language model development.
- Hands-on skills
- Develop LLM-based solutions: Lead the design, training, fine-tuning, and deployment of large language models, leveraging techniques like retrieval-augmented generation (RAG) and multi-agent based architectures.
- Build and maintain agent evaluation pipelines, including offline eval datasets, LLM-as-judge, and CI-integrated eval runs
- Codebase ownership: Build & 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: 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.
