AI Engineering Lead at Turing

AI Engineering Lead | Full Time | Bengaluru (3 Days In-Office)

Turing is seeking a seasoned AI Engineering Lead to drive the design, development, and delivery of cutting-edge Generative AI solutions. This is a hands-on leadership role for an engineer who thrives at the intersection of technical depth and strategic thinking — someone who can architect multi-agent systems, lead engineering teams, and communicate complex AI concepts to business stakeholders.

Required Skills

• 12+ years of professional software engineering experience building production-grade applications and systems.
• 2+ years of hands-on experience with LLMs and Generative AI, particularly multi-agent architectures.
• Expert proficiency in Python, LangGraph, and SQL.
• Deep expertise in architecting GenAI applications using modern frameworks and cloud services.
• Proficiency with AI coding tools such as Claude Code, Codex, Cursor, and Windsurf.
• Strong experience with AI observability and evaluation tools like LangSmith, Langfuse, or similar platforms.
• Solid working knowledge of cloud platforms — Azure, GCP, or AWS — for GenAI deployments.
• Proven track record of leading engineering teams toward a technical roadmap.
• Excellent communication skills for cross-functional collaboration with business SMEs.

Roles & Responsibilities

• Define and own the technical roadmap based on business requirements, ensuring timely delivery and customer satisfaction.
• Lead and mentor the engineering team, fostering best practices in machine learning and LLM development.
• Design robust multi-agent architectures, including supervisor-router patterns with dynamic sub-agent routing and stopping conditions.

• Lead the design, fine-tuning, and deployment of LLM-based solutions using techniques such as Retrieval-Augmented Generation (RAG) and multi-agent frameworks.
• Build and maintain agent evaluation pipelines — including offline eval datasets, LLM-as-judge, and CI-integrated evaluation runs.
• Own and maintain high-quality, scalable Python codebases using LangChain/LangGraph, with a focus on reusable components and performance.
• Deploy and optimize GenAI applications on cloud platforms with robust CI/CD processes.

• Stay actively informed on frontier AI developments — model releases, agentic architectures, evaluation methods, multimodal capabilities, and reasoning trends.
• Translate complex technical concepts into clear, structured insights for non-engineering executive audiences.
• Partner closely with product owners, data scientists, and business SMEs to define requirements and deliver impactful AI products.