💡 Siemens is inviting applications for its Off Campus Drive 2025 to hire talented Junior Software Developers specializing in AI/ML. This is an excellent opportunity for fresh graduates and experienced candidates to work on cutting-edge technologies like Natural Language Processing (NLP), Generative AI, LLM Architectures, and Cloud AI Services. Based in Bangalore, this role offers hands-on experience in developing intelligent, context-aware applications, fine-tuning AI models, and deploying scalable AI-first systems. If you are passionate about Artificial Intelligence and ready to innovate, Siemens is the perfect platform to launch or elevate your career.
| Siemens | |
| Job Role | Junior Software Developer – AI/ML |
| Qualification | B.E/B.Tech/M.E/M.Tech |
| Batch | 2022/2023/2024/2025 |
| Experience | Freshers/Experienced |
| Salary | Best in Industry |
| Location | Bangalore |
| Last Date | ASAP |
| Apply Link Below | |
|---|---|
🎯 Eligibility Criteria
✅ Bachelor’s or Master’s degree in Computer Science, AI, Machine Learning, or related fields
✅ Knowledge in AI/ML with exposure to NLP and Generative AI
✅ Understanding of LLM architectures & fine-tuning methods (LoRA, PEFT)
✅ Experience with RAG pipelines, agent-based architectures, and vector search
✅ Proficiency in Python, LangChain, Transformers, Llama Index, etc.
✅ Knowledge of cloud AI services (AWS Sagemaker, Azure AI Foundry, etc.)
✅ Familiarity with observability tools like TruEra, Arize, WhyLabs
🛠 Job Description
💡 Design & optimize NLP-driven AI solutions using cutting-edge models
💡 Build & productionize RAG pipelines for intelligent, context-aware systems
💡 Fine-tune & deploy LLMs (OpenAI, Anthropic, Falcon, LLaMA) for real-world use cases
💡 Collaborate with cross-functional teams to create scalable AI-first solutions
💡 Integrate third-party models/APIs for generative AI applications
💡 Ensure observability, explainability, and performance optimization in AI deployments
🌟 Why Join Siemens?
🚀 Work on next-gen AI/ML innovations with global impact
🌎 Collaborate with world-class engineers in a dynamic environment
📈 Continuous learning and growth opportunities in cutting-edge AI domains
🏆 Be part of a trusted, globally recognized leader in technology
🏢 About Siemens
Siemens is a global powerhouse in electrification, automation, and digitalization, driving innovation in AI, industrial automation, and energy solutions. With a strong presence in over 200 countries, Siemens empowers industries to achieve sustainable growth and technological excellence.
📂 Additional Information
🔹 Domain Advantage: Electrification, Energy, Industrial AI knowledge is a plus
🔹 Tech Stack Exposure: Lang Graph, Function Calling, Infrastructure-as-Code (Terraform/CDK)
🔹 Focus Areas: Streaming NLP, latency optimization, multi-turn dialogues
📝 How to Apply?
Interested candidates can apply online at the official Siemens careers portal before the position is filled. Apply early to secure your interview slot.
| Jobs by Qualification | |
| B.Tech | BBA |
| B.Sc | B.Com |
| M.Sc | M.Com |
| MCA | MBA |
| BCA | M.Tech |
❓ Interview Questions & Answers
1️⃣ Q: What is a RAG pipeline in AI/ML?
A: RAG (Retrieval-Augmented Generation) pipelines combine information retrieval and generative AI models to produce more accurate and context-aware responses.
2️⃣ Q: How do you fine-tune a large language model like GPT?
A: Fine-tuning involves training a pre-trained model with domain-specific data to improve performance for targeted use cases, often using methods like LoRA or PEFT.
3️⃣ Q: What is the role of embeddings in NLP?
A: Embeddings convert text into numerical vector representations that capture semantic meaning, enabling similarity search, clustering, and contextual understanding.
4️⃣ Q: Explain latency optimization in real-time NLP applications.
A: Latency optimization involves reducing response time by using efficient architectures, model compression, parallel processing, and optimized hardware usage.
5️⃣ Q: How does LangChain help in building AI applications?
A: LangChain provides a framework to connect LLMs with external tools, APIs, and data sources, making it easier to create intelligent, multi-step agent workflows.
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