MLops

Master Mind Consultancy; Chennai; On-site; INR 1,200,000 - 2,400,000; Full time

Job summary

Master Mind Consultancy is seeking an experienced AI/ML engineer to develop and maintain scalable AI/ML infrastructure for near-real-time processing and inference. You will build RESTful APIs with Python, design model training and deployment pipelines (Azure ML), and integrate models into production applications.

You will collaborate with data scientists and engineers, monitor performance, implement testing, and drive MLOps practices across the stack in a fast-paced environment.

Qualifications

• Bachelor’s degree in Computer Science, Computer Engineering, or a related field.
• Strong proficiency in Python, RESTful APIs, Flask, Pandas, NumPy, and SQLAlchemy.
• Strong understanding of data structures, algorithms, scalability, performance, and system availability.
• Knowledge of data processing, data mining, Big Data, and RDBMS technologies.
• Experience with SQL, PostgreSQL, Spark, Hive, Databricks, or Snowflake.
• Hands-on experience with Azure or other cloud platforms, preferably Azure ML.
• Knowledge of ML model training, deployment pipelines, and MLOps practices.
• Experience with Jenkins, Kafka, or other messaging and orchestration frameworks.
• Familiarity with Linux, Docker, Kubernetes, or Docker Swarm.
• Proficiency in Git and collaboration tools such as Jira and Confluence.
• Strong problem-solving, analytical, documentation, and English communication skills.

Responsibilities

• Develop and maintain scalable AI/ML infrastructure for near-real-time processing and inference.
• Build RESTful APIs and applications using Python.
• Design, implement, and maintain ML model training and deployment pipelines, primarily using Azure ML.
• Integrate ML models into production applications and optimize the MLOps lifecycle.
• Develop monitoring dashboards, performance metrics, alerts, and automated testing solutions.
• Troubleshoot data, application, and infrastructure issues to ensure system reliability.
• Work with data scientists, product owners, and engineering teams to deliver scalable AI/ML solutions.
• Process large volumes of data using distributed computing technologies.
• Evaluate new technologies, open-source frameworks, and tools for AI/ML engineering.
• Contribute to architectural design, technical documentation, and engineering best practices.
• Mentor engineers and support collaboration across cross-functional teams.

Skills

RESTful API design

English communication

Analytical thinking

Documentation

Education

Bachelor’s degree in Computer Science or related field

Tools

Python

Flask

Pandas

NumPy

SQLAlchemy

PostgreSQL

Spark

Hive

Databricks

Snowflake

Azure ML

Docker

Kubernetes

Jenkins

Kafka

Git

Jira

Confluence

Linux

Job description

• Develop and maintain scalable AI/ML infrastructure for near-real-time processing and inference.
  
• Build RESTful APIs and applications using Python.
  
• Design, implement, and maintain ML model training and deployment pipelines, primarily using Azure ML.
  
• Integrate ML models into production applications and optimize the MLOps lifecycle.
  
• Develop monitoring dashboards, performance metrics, alerts, and automated testing solutions.
  
• Troubleshoot data, application, and infrastructure issues to ensure system reliability.
  
• Work with data scientists, product owners, and engineering teams to deliver scalable AI/ML solutions.
  
• Process large volumes of data using distributed computing technologies.
  
• Evaluate new technologies, open-source frameworks, and tools for AI/ML engineering.
  
• Contribute to architectural design, technical documentation, and engineering best practices.
  
• Mentor engineers and support collaboration across cross-functional teams.

Requisites

• Bachelor’s degree in Computer Science, Computer Engineering, or a related field.
  
• Strong proficiency in Python, RESTful APIs, Flask, Pandas, NumPy, and SQLAlchemy.
  
• Strong understanding of data structures, algorithms, scalability, performance, and system availability.
  
• Knowledge of data processing, data mining, Big Data, and RDBMS technologies.
  
• Experience with SQL, PostgreSQL, Spark, Hive, Databricks, or Snowflake.
  
• Hands-on experience with Azure or other cloud platforms, preferably Azure ML.
  
• Knowledge of ML model training, deployment pipelines, and MLOps practices.
  
• Experience with Jenkins, Kafka, or other messaging and orchestration frameworks.
  
• Familiarity with Linux, Docker, Kubernetes, or Docker Swarm.
  
• Proficiency in Git and collaboration tools such as Jira and Confluence.
  
• Strong problem-solving, analytical, documentation, and English communication skills.

Skills :