Rohde & Schwarz (R&S) is Hiring AI Engineer | Freshers can Apply Now

Short Intro

Artificial Intelligence is rapidly transforming how modern engineering organisations build products, analyse data, and solve complex problems. The AI Engineer role at Rohde & Schwarz is specially suited for fresh graduates or early professionals who are interested in working at the intersection of technology, research, and real-world applications. This position offers early-career professionals an opportunity to contribute to intelligent systems while learning how AI is applied in highly technical industries such as electronics, communications, and advanced engineering solutions.

Unlike purely academic roles, this job focuses on practical impact—how algorithms, models, and intelligent systems support business and engineering outcomes. Candidates who enjoy problem-solving, logical thinking, and experimenting with data-driven approaches will find this role meaningful. It also provides a strong foundation for future careers in analytics, machine learning, and emerging domains, similar to pathways seen in sustainability / analyst jobs for freshers and other technology-driven career tracks.

Job Highlights

Job Description

The AI Engineer role at Rohde & Schwarz contributes to developing solutions, testing solutions, and then applying those intelligent solutions that support engineering, technology, and business use cases. Rather than working on generic applications, professionals in this role typically contribute to specialized environments where accuracy, innovation, and technical depth matter.

As an AI Engineer, you may work on building models, analyzing datasets, improving algorithms, and collaborating with cross-functional teams to integrate intelligent features into products or internal systems. The business impact of this work can include improving efficiency, enabling smarter decision-making, and supporting next-generation technologies.

For freshers, this role is valuable because it provides exposure to both structured problem-solving and applied AI practices. It also builds a strong foundation for long-term growth in areas such as data science, applied research, advanced analytics, and roles that overlap with domains discussed in a beginner guide to ESG / analytics and emerging technology-driven careers.

Roles & Responsibilities

• Assist in developing and testing AI or machine learning models for practical use cases
• Work with datasets to clean, structure, and analyze information for model training
• Support senior engineers in experimenting with algorithms and improving model accuracy
• Document model behavior, assumptions, and results for team understanding
• Collaborate with engineering teams to integrate AI outputs into broader systems
• Monitor model performance and suggest refinements based on observed results
• Participate in technical discussions and contribute ideas to ongoing projects
• Follow structured development practices to ensure reliability and quality

Skills Required

Technical / Tool-based Skills

• Basic understanding of Python or similar programming languages
• Familiarity with machine learning concepts such as classification or regression
• Exposure to libraries like NumPy, Pandas, or introductory ML frameworks (helpful but not mandatory)
• Understanding of data handling and simple visualization techniques
• Willingness to learn new tools and platforms used internally

Analytical / Business Skills

• Logical thinking and structured problem-solving ability
• Curiosity to explore how models behave and why outcomes change
• Ability to understand requirements and translate them into technical tasks that can be used for further development steps
• Attention to detail when working with data and results
• Communication skills to explain technical ideas simply within teams

Eligibility Criteria

• Bachelor’s, Master’s, or PhD degree in a relevant discipline (Preferred)
• Fresh graduates are eligible to apply
• Academic background in engineering, computer science, mathematics, or related fields is beneficial
• Interest in artificial intelligence, data-driven problem-solving, or applied research
• Willing to learn and adapt fast in a technical environment

Salary Details

The expected compensation for this role is around ₹4.5 LPA, based on available market estimates. Actual salary may vary depending on educational background, skill level, and internal company evaluation.

Selection Process (Expanded)

1. Application Screening
Applications are reviewed for academic background and relevance to AI or technical roles.
Preparation tips:

• Highlight projects, coursework, or certifications related to AI, data, or programming
• Ensure your resume clearly explains what you worked on and what you learned

2. Technical Assessment (if applicable)
May involve basic programming, problem-solving, or conceptual questions.
Preparation tips:

• Revise Python basics, data structures, and introductory ML concepts
• Practice explaining your project approach rather than memorizing definitions

3. Technical Interview
Focuses on your understanding of AI fundamentals, logic, and learning mindset.
Preparation tips:

• Be honest about your level but show strong curiosity and clarity of thought
• Prepare to explain your final-year project or internships in depth

4. HR Discussion
Covers role understanding, communication, and career alignment.
Preparation tips:

• Explain why AI interests you and how you see yourself growing in this field
• Show willingness to learn rather than claiming expertise too early

What This Role Means in the 2025–2026 Job Market

By 2025–2026, organizations across industries are embedding AI into everyday systems—from automation tools to data analysis pipelines and decision-support platforms. This has increased demand for engineers who not only understand theory but can also apply concepts practically.

Roles like AI Engineer are becoming more structured entry points for fresh graduates. Companies now expect early-career professionals to understand core concepts and grow through real-world exposure rather than mastering everything upfront.

This shift is similar to trends seen in sustainability / analyst jobs for freshers, where organizations invest in building foundational skills and domain understanding over time. Employers are increasingly valuing learning agility, problem-solving ability, and structured thinking.

For candidates entering the workforce, starting in an AI-focused role provides long-term relevance. Even if you later move toward analytics, business intelligence, or consulting, the exposure to AI and data-driven systems becomes a strong differentiator.

Tips for Freshers

Start With Strong Fundamentals, Not Buzzwords
Many freshers focus too much on flashy terms like “deep learning” or “GenAI” without building core understanding. Instead, strengthen basics such as Python, statistics, data structures, and logical reasoning. These fundamentals matter far more in interviews and on the job.

Use Projects to Demonstrate Real Learning
Employers care less about certificates and more about what you have built. A simple project—like analyzing a dataset, building a recommendation model, or visualizing insights—can demonstrate seriousness. Be ready to explain your thought process, challenges, and improvements.

Understand the Difference Between Theory and Application
Knowing definitions is not enough. Try to understand why an algorithm behaves in a certain way, how data quality affects outcomes, and what happens when assumptions break. This mindset aligns closely with professional expectations in technical roles.

Learn to Communicate Technical Ideas Simply
Even in technical roles, communication matters. You should be able to explain your project to a non-technical friend. This skill is also critical for future roles that overlap with consulting, analytics, or even business analyst interview preparation.

Avoid Comparing Your Journey With Others
Some candidates feel discouraged by peers who appear more advanced. Remember that companies hiring freshers expect potential, not perfection. Focus on consistency rather than trying to master everything at once.

Treat Your First Role as a Learning Platform
This job is not about having all the answers first and right. It is about building discipline, understanding real-world constraints, and developing the ability to learn continuously. Professionals who adopt this mindset tend to progress faster over time.

Helpful Resources

Google Machine Learning Crash Course
Source: Google
Offers a structured introduction to machine learning concepts with practical explanations.
Usage tip: Focus on understanding concepts rather than rushing through modules.

Kaggle Learn
Source: Kaggle
Provides beginner-friendly courses on Python, data analysis, and machine learning.
Usage tip: Practice notebooks alongside lessons to build hands-on confidence.

NPTEL – Introduction to Artificial Intelligence
Source: IITs via NPTEL
Academic-quality lectures that build strong conceptual foundations.
Usage tip: Use these courses to strengthen theory for interviews.

Fast.ai Practical Deep Learning
Source: Fast.ai
Focuses on learning by building, even with limited background.
Usage tip: Do not try to complete everything; pick modules aligned with your level.

Interview Insights

Interviewers for AI Engineer roles typically evaluate three areas: clarity of fundamentals, problem-solving mindset, and willingness to learn. You may be asked about your projects, how you approached data, or why you chose certain methods.

They are less interested in perfect answers and more interested in how you think. If you faced issues during a project, explaining what went wrong and how you fixed it often creates a stronger impression than showcasing only success.

You might also face scenario-based questions such as how you would handle noisy data or improve a weak model. Approach such questions logically rather than trying to recall textbook answers. This approach is similar to expectations seen in business analyst interview preparation, where structured reasoning matters more than memorization.

How to Apply

Every candidate must apply only through the official Rohde & Schwarz careers portal or the job portal choosen by the company. Before applying, review the job description on the company website to confirm eligibility, expectations, and role details.

apply on official Rohde & Schwarz careers portal.

About Company

The company supports industries such as telecommunications, aerospace, defence, and research institutions.

Its work often involves solving complex technical challenges where accuracy, innovation, and reliability are critical. This makes it a respected environment for engineers and technical professionals who want to work on meaningful, high-impact problems.

In India, Rohde & Schwarz continues to expand its footprint, contributing to both global projects and local innovation. The company’s emphasis on precision engineering and long-term technological development provides employees with opportunities to build deep technical expertise while working in collaborative, knowledge-driven teams.

Disclaimer

This article is published for informational purposes only. We are not recruiters, hiring partners, or representatives of Rohde & Schwarz. We do not charge any fees for job information nor promote any third party to do so. Candidates must verify all details directly from the official Rohde & Schwarz careers website before applying. Always avoid paying anyone who claims to offer job opportunities in exchange for money.