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.