onsite
AI Engineering Lead
AI Engineering Lead
Turing is looking for an AI Engineering Lead with strong GenAI experience to solve business problems for Fortune 500 customers. This role involves leading a team of engineers, architecting GenAI applications, and hands-on development and deployment of LLM-based solutions using Python, LangGraph, SQL, and various cloud services. The lead will be responsible for the technical roadmap and ensuring customer satisfaction through timely execution.
About the role
About the Role
Turing is seeking experienced GenAI professionals to join their team, focused on solving business problems for Fortune 500 customers. As a key member of the Turing Intelligence delivery organization, you will be part of a GenAI project and will lead a team of Turing engineers with diverse skill sets. The Turing GenAI delivery organization has a track record of implementing industry-leading multi-agent LLM systems and LLM deployments for major enterprises.
Required Skills
- 12+ years of professional experience in software engineering and building applications/systems.
- 2+ years of hands-on experience with how LLMs work and 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 and cloud services.
- Expert proficiency in using AI tools like claude code, codex, cursor, windsurf, and similar.
- Expert proficiency in AI observability and evaluation tools like Langsmith, Langfuse, or similar.
- Good proficiency in using various cloud services from Azure, GCP, or AWS for building 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 its delivery.
- Lead the engineering team toward a technical roadmap and ensure timely execution 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 and 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.