MLOps Engineer

Adept Global; Bengaluru; On-site; INR 1,500,000 - 2,300,000; Full time

Job summary

Adept Global in Bengaluru seeks a Software Engineer - MLOps to build scalable Python-based backend services, ML pipelines, and data processing systems powering an AI-driven warehouse intelligence platform.

You will deploy and optimize models with NVIDIA Triton, work with ML, Platform, and DevOps teams, and own features from design to production, emphasizing scalability and observability.

Qualifications

• 2+ years of experience developing production-grade Python applications.
• Hands-on experience with NVIDIA Triton Inference Server, including performance optimization and 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.

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.

Skills

Python programming

Distributed systems

MLOps

API design

CI/CD

Kafka

Performance optimization

Tools

NVIDIA Triton Inference Server

Kafka

Docker

Git

CI/CD

MLflow

DVC

Airflow

FastAPI

YAML

JSON

Ansible

Job description

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 collaborative, 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:
• 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 environment and enjoy learning new technologies.

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.