Sr. MLOps Engineer

PradeepIT Consulting Services Pvt Ltd; Bengaluru; On-site; INR 1,000,000 - 1,500,000; Full time

Job summary

A technology consulting firm in Bengaluru seeks a Data Engineer specializing in MLOps. The role involves reviewing data science models, optimizing code, and implementing cloud solutions on Azure. Ideal candidates have 4-7 years of experience, strong Python skills, and familiarity with MLOps frameworks like Kubeflow. This position offers an opportunity to work with advanced data engineering practices in a collaborative environment.

Qualifications

• 4-7 years of experience in MLOps and Data engineering.
• Rich hands-on experience of 3+ years in writing object-oriented code using Python.
• Min 3 years of MLOps experience including model versioning and deployment.

Responsibilities

• Review data science models, optimize code, and manage deployment.
• Implement cloud solutions and build MLOps on Azure.
• Test and validate data science models and automate tests.

Skills

MLOps and Data engineering experience

Object-oriented programming in Python

Using MLOps frameworks (Kubeflow, MLFlow, Airflow)

Experience with Azure cloud services

Strong DevOps mentality

Tools

Docker

Kubernetes

Terraform

Job description

Job Description

Total Exp in years - 4 7

Responsibilities

• Data science model review, run the code refactoring and optimization, containerization, deployment, versioning, and monitoring of its quality.
• Design and implement cloud solutions, build MLOps on cloud (preferably Azure)
• Work with workflow orchestration tools like Kubeflow, Airflow, Argo or similar tools
• Data science models testing, validation and tests automation.
• Communicate with a team of data scientists, data engineers and architect, document the processes.

Mandatory Skills

• 4 7 years of experience in MLOps and Data engineering
• Rich hands-on experience of 3+ years in writing object-oriented code using python
• Min 3 years of MLOps experience (Including model versioning, model and data lineage, monitoring, model hosting and deployment, scalability, orchestration, continuous learning, Automated pipelines)
• Understanding of Data Structures, Data Systems and software architecture
• Experience in using MLOps frameworks like Kubeflow, MLFlow, Airflow Pipelines for building, deploying, and managing multi-step ML workflows based on Docker containers and Kubernetes.
• Experience with Azure cloud services, Cosmos DB, Streaming Analytics, IoT messaging capacity, Azure functions, Azure compute environments, etc.
• Exposure to deep learning approaches and modeling frameworks (PyTorch, Tensorflow, Keras, etc.)
• Strong DevOps mentality: Knowledge of making a complicated pipeline simple and easy to maintain, with proven experience of Terraform/Spark