Adept Global
Website:
adeptglobal.com
Company:
https://www.linkedin.com/company/adeptglobal
Seniority: Entry level
Industries: Artificial Intelligence, Warehousing and Storage, and Robotics Engineering
Job details:
Software Engineer – MLOps
Experience: 2+ Years
Location: Bangalore, India
Company Industry: AI / Robotics / Warehouse Automation / SaaS
About the Role:
We are looking for a Python Engineer – MLOps to build and scale backend systems powering an AI-driven warehouse intelligence platform. You will work across MLOps, data pipelines, configuration management, and distributed systems, collaborating closely with ML, Platform, and DevOps teams.
Key Responsibilities:
- Design and develop Python-based tools and backend services for MLOps and data pipelines.
- Work with NVIDIA Triton Inference Server for model deployment and performance optimization.
- Build scalable, modular components for high-throughput data processing using Kafka.
- Develop and maintain production-grade services using FastAPI or similar frameworks.
- Collaborate with ML Engineers, Platform Developers, and DevOps teams.
- Contribute to system architecture, design patterns, scalability, and performance.
- Implement CI/CD, containerization, monitoring, and deployment workflows.
- Work with structured configurations such as YAML/JSON and infrastructure-as-code tools.
- Own features end-to-end, from design and development through deployment and monitoring.
What We’re Looking For:
- 2+ years of production experience with Python.
- Hands-on experience with NVIDIA Triton Inference Server is essential.
- Strong understanding of software architecture, distributed systems, scalability, and fault tolerance.
- Experience with Kafka or similar messaging systems.
- Good knowledge of Git, CI/CD, and Docker.
- Exposure to MLOps tools/frameworks such as MLflow, DVC, or Airflow.
- Experience with FastAPI or similar Python web frameworks.
- Exposure to Ansible, YAML/JSON, or infrastructure-as-code.
- Strong problem-solving skills with a design-oriented and ownership-driven approach
Click on Apply to know more.
