Principal AI Engineer - Hybrid (Marathalli)

Turing Global India; Bengaluru; Hybrid; INR 3,500,000 - 7,000,000; Full time

Job summary

Turing is seeking a Principal GenAI Engineer to lead enterprise-scale AI initiatives for Fortune 500 clients. You will craft scalable, production-grade GenAI systems powered by LLMs, RAG, and multi-agent architectures.

Based in Bengaluru, you will guide a cross-functional team toward a technical roadmap, mentor engineers, and ensure robust CI/CD and cloud deployments. Strong collaboration with business SMEs is essential.

Qualifications

• 10+ years of professional software engineering experience.
• 2+ years hands-on GenAI/LLM experience with multi-agent systems.
• Expert in Python, LangGraph and SQL.
• Architect GenAI apps using frameworks and cloud services.
• Proficient with AI tools like Claude Code, Codex, Cursor, Windsurf.
• Proficient in AI observability tools LangSmith, Langfuse.
• Experience with Azure, GCP, or AWS cloud services.
• Strong ability to drive a technical roadmap and collaborate with SMEs.

Responsibilities

• Solutioning & Lead: Build the technical roadmap from business requirements and own delivery.
• Lead the engineering team toward a roadmap and ensure timely execution.
• Design robust multi-agent architectures with dynamic sub-agent routing.
• Mentor and share knowledge on ML and LLM development.
• Hands-on: Develop LLM-based solutions with RAG and multi-agent architectures.
• Build agent evaluation pipelines and CI-integrated eval runs.
• Code ownership: Python (LangChain/LangGraph) and SQL, focused on scalability.
• Cloud deployment: Deploy GenAI apps on Azure, GCP, or AWS with CI/CD.

Skills

10+ years software experience

2+ years GenAI/LLM

Python

LangGraph

SQL

GenAI architecture

AI observability tools LangSmith/Langf

Azure/GCP/AWS cloud

Technical roadmap leadership

Communication with SMEs

Tools

LangChain

LangGraph

Claude Code

Codex

Cursor

Windsurf

LangSmith

Langfuse

Azure

GCP

AWS

Job description

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.