Designation: Software Engineer – MLOps

? Location: Bangalore, India

? Experience: 2+ Years

? Employment Type: Full-Time

? Work Mode: In-Office

About the Client

Our client is a fast-growing technology startup transforming warehouse inventory management through AI-powered inventory scanning and intelligent automation. The engineering team is highly team-oriented, working across MLOps, distributed systems, backend infrastructure, and large-scale data processing to build reliable, production-grade platforms.

Role Overview

We are looking for a Software Engineer – MLOps with strong Python development experience and a solid understanding of software architecture, distributed systems, and ML infrastructure. In this role, you will build scalable backend services, MLOps pipelines, and data processing systems that power an AI-driven warehouse intelligence platform.

This is an excellent opportunity for engineers who enjoy solving complex infrastructure challenges, working with cross-functional teams, and owning features from design through production deployment.

Key Responsibilities

- Design, develop, and maintain Python-based services and tools for MLOps, data pipelines, and configuration management.
- Build scalable, modular backend components for high-throughput data processing using technologies such as Kafka.
- Deploy, optimize, and maintain machine learning models using NVIDIA Triton Inference Server.
- Collaborate with ML Engineers, Platform Engineers, and DevOps teams to build reliable production infrastructure.
- Contribute to software architecture discussions, focusing on scalability, performance, and maintainability.
- Write clean, modular, well-tested,



and well-documented code following software engineering best practices.
- Take end-to-end ownership of features—from design and implementation to deployment, monitoring, and continuous improvement.

Required Skills & Experience

- 2+ years of experience developing production-grade Python applications.
- Hands-on experience with NVIDIA Triton Inference Server , including:
- Model deployment
- Performance optimization
- Production integration
- Strong understanding of software design principles, modular architecture, scalability, and fault tolerance.
- Experience with Kafka or similar distributed messaging systems.
- Experience with Git , CI/CD pipelines, and containerization using Docker .
- Exposure to MLOps tools such as:
- MLflow
- DVC
- Airflow
- Experience building APIs using FastAPI or similar Python web frameworks.
- Familiarity with Infrastructure as Code and configuration management using YAML , JSON , or Ansible .

Preferred Qualifications

- Experience building distributed systems and large-scale backend applications.
- Understanding of production ML workflows and model lifecycle management.
- Knowledge of monitoring, logging, and performance tuning for backend services.
- Strong debugging and problem-solving skills.

What We're Looking For Design-Oriented

You value clean architecture, reusable components, and building systems that are easy to maintain and scale.

Adaptable

You're comfortable working in a fast-paced startup setting and enjoy learning new technologies.

Collaborative

You work effectively with cross-functional teams to deliver high-quality engineering solutions.

Detail-Oriented

You think critically about edge cases, reliability, observability, and production readiness.

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