MLops Engineer

Software Engineer – MLOps

Location: Bangalore, India

Experience Level: 2+ years

Type: Full-Time

Work Mode: In-office

About Our Client

Our client is a fast-growing startup revolutionizing inventory scanning in the warehouse industry. The team is agile, curious, and deeply passionate about building reliable and scalable systems across the stack—from MLOps pipelines to robust configuration infrastructure and distributed data pipelines.

Role Overview

We are looking for a Python Engineer who thrives in dynamic environments and has a strong grasp of software architecture and distributed systems. You will design and build core backend components that power an AI-driven warehouse intelligence platform. This is a high-impact role with opportunities to solve complex systems problems, work closely with cross-functional teams, and drive engineering best practices.

What You'll Do

- Design, implement, and maintain Python-based tools and services across MLOps, data pipelines, and configuration management. - Architect scalable and modular components for high-throughput data processing using technologies like Kafka. - Collaborate with ML engineers, platform developers, and DevOps to build efficient and maintainable infrastructure. - Contribute to engineering discussions on design patterns, performance, and scalability. - Write clean, modular, and well-documented code following solid software engineering principles. - Take ownership of features end-to-end—from ideation and design to deployment and monitoring.

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What We're Looking For

- 2+ years of experience developing and maintaining Python applications in production environments. - Hands-on experience with NVIDIA Triton Inference Server, including model deployment, performance optimization, and integration into production workflows. - Strong software design skills with a focus on modularity, scalability, and fault-tolerance. - Experience with Kafka or similar distributed messaging systems. - Solid understanding of version control (Git), CI/CD workflows, and containerization tools like Docker. - Exposure to MLOps frameworks (e.g., MLflow, DVC, Airflow). - Experience with FastAPI or similar Python-based web frameworks. - Experience with infrastructure-as-code and structured config formats (e.g., YAML, JSON, Ansible).

You’re a Great Fit If You Are

- Design-oriented – You care about abstraction, maintainability, and writing systems that are easy to reason about. - Adaptable – You can switch contexts easily and learn new technologies with confidence. - Collaborative – You enjoy working with cross-functional teams to deliver end-to-end features. - Detail-conscious – You think deeply about edge cases, system behavior, and monitoring.