Backend AI Developer

3 days ago

Chennai, Tamil Nadu, India beBeeMachineLearning Full time ₹ 75,000 - ₹ 95,000

AI Engineering Role Overview

We are seeking an experienced Software Engineer to co-assist in designing, building and scaling production-ready AI systems. This combines strong software engineering fundamentals with applied machine learning expertise.

This is a full-time hybrid role based in Chennai but offers the flexibility of working from home for part of the time. The successful candidate will be responsible for developing and maintaining backend architecture, software development, implementing machine learning algorithms, ensuring efficient performance, and collaborating with front-end developers to integrate user-facing elements.

The engineer will build robust backend systems & APIs to deploy and scale ML solutions. They will work with cloud-native infrastructure (AWS) to design cost-efficient, high-availability ML pipelines and scalable cloud supported backend solutions.

They will implement ML lifecycle workflows — from experiment tracking (MLflow/Kubeflow/DVC) to orchestration (Airflow/Prefect). They will contribute to end-to-end product development: data ingestion, preprocessing, model serving, monitoring and continuous improvement.

They will collaborate with founders and cross-functional teams in a startup fast-paced environment. They will work with founding engineers to embed intelligent agents, predictive models, and decision-making capabilities into backend workflows using Predictive learning through ML algorithms.

The ideal candidate has a Bachelor's / Master's degree in CS, Data Science, AI/ML and 1-3 years of experience in backend development, with a focus on building scalable, distributed systems.

They have programming languages: MERN, Python, Golang. Java is optional but definitely a plus.

They have databases knowledge: Strong experience with PostgreSQL + familiarity with NoSQL/MongoDB.

They have MLOps & Infrastructure knowledge: Knowledge of MLflow, Kubeflow, DVC, CI/CD pipelines, Docker, Kubernetes.

They have Cloud & Infra knowledge: AWS SageMaker/Bedrock, GCP Vertex AI, Azure AI.

They have Versioning & Collaboration knowledge: Git, GitHub/GitLab, code reviews, unit testing.

They have ability to write clean, modular, and scalable code with strong debugging & testing discipline.

They design backend APIs to support AI-driven process optimization and NLP-powered automations.

They have a startup-ready mindset: adaptable, curious, hands-on.

They have strong ownership + problem-solving.

They are eager to push Generative AI into real-world adoption.

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