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Software Developer - Train IT

Siemens

  • Pune, India
  • β‚Ή10 LPA
  • 5+ Years
  • Posted Today
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About the role

Siemens Limited is a global technology powerhouse with a legacy of more than 170 years, delivering cutting‑edge solutions across electrification, automation, and digitalisation. In India, Siemens operates across multiple verticals, from smart infrastructure to industrial automation, and its Mobility division is a leader in rail and transport solutions. The company’s culture blends engineering excellence with a strong focus on sustainability, innovation, and employee growth, making it a preferred employer for aspiring technologists.

The Mobility business unit, an independent arm of Siemens AG, designs and manufactures rail vehicles, signalling systems, and intelligent traffic solutions. With a presence in over 200 countries, Siemens Mobility helps cities and rail operators improve safety, reliability, and passenger experience. Working at Siemens means exposure to large‑scale, mission‑critical projects that shape the future of transportation.

Role Summary

As a Software Developer – Train IT, you will be part of the embedded‑software team that builds diagnostic and data‑analysis platforms for next‑generation locomotives. You will translate vehicle‑level requirements into robust Linux‑based software, integrate external devices, and ensure seamless communication between on‑board systems and remote monitoring stations. The role demands a blend of systems thinking, hands‑on coding, and cross‑functional collaboration.

Key Responsibilities

  • Define system requirements and interface specifications for vehicle diagnostics.
  • Gather, analyse, and structure software requirements to create solution concepts.
  • Design complete software architectures for remote data transmission.
  • Integrate and develop external devices for diagnostics management and landside communication.
  • Develop, test, and maintain embedded applications on Linux using C++ and Python.
  • Provide technical advisory support to fellow engineers throughout the development lifecycle.
  • Conduct software integration, functional testing, and validation on actual locomotives.
  • Implement upgrades and new features following the V‑model development approach.
  • Manage configuration, change requests, and documentation in line with industry standards.
  • Collaborate with global teams and support field deployments, including 15‑20% travel.

Tech Stack: Linux OS, C++, Python, Embedded Systems, V‑model, IEC 50128/61131/61375 standards, TCN protocol, Git, Jenkins, and basic knowledge of requirement‑engineering tools.

Growth Path: Starting as a Software Developer, you can progress to Senior Engineer, Technical Lead, and eventually Architecture or Program Management roles within Siemens Mobility, leveraging global project exposure and continuous learning programs.

Why Join Siemens: You will work on high‑impact rail projects that touch millions of passengers daily, benefit from a strong mentorship ecosystem, and enjoy a balanced hybrid work model after probation. Siemens’ commitment to diversity, innovation, and employee well‑being makes it an ideal place to launch a rewarding engineering career.

Eligibility & process

Who can apply

Bachelor's or Master's degree in Electrical Engineering, Electronics, Computer Science, or a comparable discipline; minimum 60% aggregate (or CGPA 6.5/10); fresh graduates or candidates from the 2024‑2027 batches; no active backlogs at the time of joining; strong programming fundamentals in C++/Python; willingness to travel up to 20% of the time.

Skills & stack

C++ Python Linux Embedded Systems Requirement Engineering V-Model Iec 50128 Iec 61131

Selection process

  1. Round 1: Online aptitude test (logical reasoning, quantitative, verbal)
  2. Round 2: Technical interview (coding, system design, domain knowledge)
  3. Round 3: HR interview (fitment, motivation, salary discussion)

Prep kit

19 resources
Study material 4
Company-specific guides 4
Likely coding questions 4
Round-wise strategy 4
Round 1: Online Assessment (OA) Aptitude, Quantitative Logic & 2 Coding Problems Focus on accuracy and speed. Practice arrays, strings, and standard arithmetic puzzles.
Round 2: Technical Interview 1 Core Tech Stack (C++, Python, Linux, Embedded Systems, Requirement Engineering, V-Model, Iec 50128, Iec 61131) & Live Code Tracing Explain your thought process aloud. Analyze time & space complexities before coding.
Round 3: System Design & Problem Solving Database Schemas, APIs & Architecture Basics Clarify edge cases, diagram schemas cleanly, and discuss scalability trade-offs.
Round 4: HR & Cultural Fit Discussion Siemens Core Values, Learning Agility & Offer Terms Demonstrate passion, strong communication, and readiness for full-time collaboration.
Technical questions 4
What is the bias-variance tradeoff and how do you prevent overfitting? Answer
Model answer: High bias leads to underfitting (oversimplified model), high variance leads to overfitting (captures noise). Mitigate using L1/L2 Regularization, Dropout, Cross-Validation, and data augmentation.
Explain the difference between Precision, Recall, and F1-Score. Answer
Model answer: Precision = TP / (TP + FP) (correctness of positive predictions). Recall = TP / (TP + FN) (coverage of actual positives). F1-Score is the harmonic mean of Precision and Recall.
How does Gradient Descent work and what is the role of Learning Rate? Answer
Model answer: It optimizes loss functions by iteratively moving weights in the direction of negative gradient. A large learning rate may overshoot the minimum; a small rate causes slow convergence.
What is the difference between Supervised, Unsupervised, and Self-Supervised learning? Answer
Model answer: Supervised uses labeled data (X -> y). Unsupervised finds hidden patterns in unlabeled data (clustering/PCA). Self-supervised generates labels from input data (e.g. masked language modeling in BERT/Transformers).
Behavioural & HR questions 3
Why do you want to join Siemens as a Software Developer - Train IT?
Highlight Siemens's market reputation, recent tech innovations, and how your skills in C++ directly solve their team's objectives.
Describe a challenging bug or academic project roadblock and how you resolved it.
Use the STAR method: Situation (project context), Task (what needed solving), Action (specific tools/logic applied), Result (quantifiable positive outcome).
How do you handle strict deadlines or sudden scope changes?
Explain your prioritization strategy, proactive communication with mentors/peers, and agile mindset.
Siemens β‚Ή10 LPA
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