Backend Engineering Lead

We are seeking a highly skilled and hands-on

Engineering Manager – Backend (Python)

to join our engineering team in Ahmedabad. The ideal candidate will bring deep technical expertise in building scalable backend systems, proficiency with modern cloud-native architectures, and proven experience leading high-performing engineering teams. This individual must be comfortable contributing to production-quality code, driving architectural decisions, and supporting the development of AI-enabled applications by leveraging FastAPI, LangGraph/Google ADK, and Gemini Enterprise on GCP.

The Engineering Manager will oversee a team of backend engineers while remaining actively involved in technical execution. This is an exciting opportunity to lead mission-critical product engineering initiatives and build state-of-the-art AI-driven systems within a dynamic and innovative environment at Adani. Experience 9–13 years

of experience in product-based technology organizations. Minimum

5 years of hands-on backend engineering

experience using Python. Served as an

Engineering Manager or Technical Lead

managing 10–15 engineers. Demonstrated experience designing scalable microservices, cloud architectures, and distributed systems. Strong exposure to building AI/LLM-enabled applications is preferred. Roles and Responsibilities As an Engineering Manager, you will be responsible for: Leadership & Delivery Driving end-to-end design, development, testing, deployment, and maintenance of backend services and AI-enabled solutions. Collaborating closely with product, architecture, data science, and AI teams to deliver high-impact features. Hands-on Engineering Actively contributing to backend development using

Python, FastAPI

, and modular service design. Designing, reviewing, and writing

production-grade code

, ensuring adherence to best practices and coding standards. Creating skeletons, frameworks, and reusable components that accelerate backend engineering velocity. Performing in-depth

code reviews

, debugging complex issues, and guiding engineers on technical excellence. Architecture & AI Platform Integration Architecting scalable, secure, high-performing backend systems on GCP (Cloud Run, GKE, Cloud SQL). Implementing AI/LLM workflows using

LangGraph or Google ADK

, integrating with

Gemini Enterprise

APIs. Collaborating with AI/ML teams to integrate models into microservices with optimal latency and reliability. Ensuring the backend architecture supports future scalability and multi-tenant enterprise use cases. Strategic Contributions Acting as a thought leader in backend architecture, cloud-native design, and AI-driven application development. Contributing to roadmap planning, resource allocation, and execution strategy aligned with business objectives. Staying up to date with emerging Python frameworks, LLM orchestration technologies, and GCP innovations. Education Qualification Bachelor’s or Master’s degree in Computer Science, Engineering, or a related technical discipline. Behavioural Skills Strong leadership capabilities and experience managing multi-disciplinary technical teams. Excellent communication and stakeholder management skills. Strategic, analytical, and problem-solving mindset. Demonstrated ability to thrive in a fast-paced, innovation-driven environment. High adaptability and openness to continuous learning. Technical Skills Expertise in

Python backend engineering

, FastAPI, asynchronous programming, and microservices. Strong understanding of

LangGraph or Google ADK

for LLM workflow orchestration. Proficiency with

GCP services

including Cloud Run, GKE, Cloud SQL, IAM, VPC, Pub/Sub, and Cloud Storage. Experience integrating AI/LLM systems such as

Gemini Enterprise

into backend applications. Strong fundamentals in system design, architecture, scalability, and performance optimization. Hands-on exposure to

DevOps

, CI/CD pipelines, Docker, Kubernetes, and cloud-native best practices. Non-Negotiable Skills Demonstrable

hands-on backend engineering

expertise in Python and FastAPI. Proven experience managing

10–15-member engineering teams

. Strong grounding in cloud-native architecture and production deployment workflows. Ability to contribute directly to development while leading teams effectively.