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Applied Computing
EngineeringπŸ’Ό FullTime

ML Engineer at Applied Computing

Applied Computing
β€’2 months ago
Location

Not specified

Salary

Competitive

Apply By

ASAP

Views

38

Eligible Batches

πŸŽ“ Fresher BatchπŸŽ“ 2025 BatchπŸŽ“ 2026 Batch

Job Description

Join Applied Computing as a forward-deployed ML Engineer and drive innovation in machine learning technologies. This role offers a competitive salary and the opportunity to work on cutting-edge projects. Apply now to take your career to the next level!

Roles & Responsibilities

Design and develop machine learning models using various ML frameworks Deploy and maintain ML-enabled applications in a production environment Collaborate with cross-functional teams to identify and prioritize ML project requirements

Requirements

Strong background in machine learning, deep learning, and software engineering Experience with TensorFlow, PyTorch, or other popular ML frameworks Excellent programming skills in languages like Python or R

Nice to Have

Kubernetes/EKS Dockerised microservices RabbitMQ

Eligibility Criteria

B.Tech/B.E. 2025/2026 Batch

Tech Stack & Ecosystem

PyTorchKubernetesDockerPythonFastAPIKafkaRabbitMQOpenAIClaudeGeminiMLDeepLearning

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Quick Info

TypeFullTime
RoleSDE
Work Mode🏒 On-site
Eligible Batches
Fresher20252026

Tech Stack

PyTorchKubernetesDockerPythonFastAPIKafkaRabbitMQOpenAI+4

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Career Advice

Company Insights

Working at Applied Computing as an ML Engineer offers a unique opportunity to be at the forefront of machine learning innovation. Our team of experts collaborates on cutting-edge projects, applying ML to real-world problems and driving business growth. With access to state-of-the-art tools and technologies, you'll have the chance to develop your skills and take on challenging projects that will shape the future of AI and ML.

Interview Guide

To ace the ML Engineer interview at Applied Computing, focus on showcasing your hands-on experience with popular ML frameworks and libraries. Be prepared to walk through code snippets and explain your thought process behind designing and deploying ML models. Additionally, demonstrate your understanding of ML concepts and applications, including deep learning, natural language processing, and computer vision. Practice explaining complex technical concepts in simple terms and prepare to discuss your experience with Agile development methodologies and collaboration tools.