Company Overview

Simplismart enables businesses to build a scalable production-grade AI system and manage the development lifecycle without writing a single line of code. Our platform allows users, from amateurs to experts, to train and monitor ML models collaboratively on almost any kind of data or use-case. By simplifying the deployment of deep learning models, we help businesses save time and reduce engineering costs significantly. Simplismart is based in Bengaluru and belongs to the Technology, Information, and Internet industry. For more information, visit https://simplismart.ai.

Job Overview

Simplismart is seeking a Mid-Level Machine Learning Engineer to join our team in Bangalore. This full-time role involves developing and optimizing machine learning models and their deployment. The ideal candidate should have a maximum of 6 years of relevant work experience and be proficient in both developing machine learning algorithms and managing MLOps practices.

Qualifications and Skills

• Proficient in Machine Learning (Mandatory skill) with a strong understanding of various ML algorithms and techniques.
• Strong programming skills in Python (Mandatory skill) with experience in libraries such as TensorFlow, PyTorch, or Scikit-Learn.
• Hands-on experience with MLOps (Mandatory skill) to streamline the deployment and monitoring of ML models.
• Proficiency in C++ for performance-focused development and implementing complex algorithms.
• Knowledge of CUDA for optimizing model training on GPUs and improving computational efficiency.
• Experience with Large Language Models (LLM) and their application in natural language processing tasks.
• Understanding of Large Language Model Operations (LLMOps) for maintaining and scaling LLM applications.
• Familiarity with Kubernetes for container orchestration and managing deployed ML models in a cloud environment.

Roles and Responsibilities

• Develop and fine-tune machine learning models to meet the specific needs of various business use-cases.
• Collaborate with cross-functional teams to integrate ML models into production environments.
• Implement MLOps practices to automate the deployment, monitoring, and management of ML models.
• Optimize ML models using CUDA to enhance training speed and computational efficiency.
• Maintain and update large language models (LLMs) to ensure they perform effectively in real-world applications.
• Utilize Kubernetes for managing containerized applications and scaling ML models in a cloud environment.
• Ensure all ML models adhere to best practices and guidelines for production-grade software development.
• Stay updated with the latest advancements in machine learning, MLOps, and related technologies.