AI/ML Engineer

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Qualcomm is hiring AI/ML Engineers in Chennai for professionals with 1–3 years of experience across Machine Learning, NLP and Data Engineering. This is an exciting opportunity for engineers who want to work at the intersection of Generative AI, LLMs, RAG, data engineering and production-grade Python development. The opening is listed under Job ID 3090305 with Qualcomm India Private Limited. The role sits within the Engineering Group – Software Engineering organization and focuses on independently building scalable ML/data solutions. Location: Chennai Experience: 1–3 years Role: AI/ML Engineer Job ID: 3090305 Company: Qualcomm India Private Limited Education: Bachelor's degree in Engineering, Information Systems, Computer Science or related field About Qualcomm Qualcomm is a global technology company known for its work in wireless connectivity, high-performance computing, AI and semiconductor technologies. Its technology powers intelligent and connected experiences across smartphones, automotive, IoT, PCs and other devices. Qualcomm describes its mission as engineering human progress through intelligent computing, AI, high-performance and low-power technologies. For AI/ML professionals, Qualcomm offers an especially interesting environment because AI is increasingly integrated into its broader computing and connectivity ecosystem. Why This Role Is Good for Your Experience This opportunity is valuable because it goes beyond traditional model development. The JD combines ML engineering + NLP + Generative AI + data engineering + backend development. You could gain practical exposure to: LLM fine-tuning and NLP Retrieval-Augmented Generation Transformers and BERT Production ML pipelines Python backend engineering Spark and PySpark SQL and NoSQL databases AWS and Azure ML platforms FastAPI, Flask and Django ETL and data ingestion Model deployment and monitoring The role also emphasizes end-to-end ownership, meaning you aren't simply expected to execute predefined tasks. You'll design solutions, build them, integrate them and work with multiple engineering teams. That combination can position you for future roles such as ML Engineer, AI Engineer, GenAI Engineer, NLP Engineer, Data Engineer or ML Platform Engineer. Skills Required Core ML & AI Machine Learning Deep Learning NLP Transformers BERT LLM fine-tuning RAG Model deployment Scikit-learn TensorFlow PyTorch GenAI & RAG LangChain LlamaIndex FAISS Milvus Hugging Face Embeddings Vector databases Prompt engineering RAG evaluation Data Engineering Python SQL PostgreSQL MongoDB Cassandra Spark PySpark Hadoop ETL/ELT Data validation Data modeling Backend & Cloud FastAPI Flask/Django REST APIs Pandas NumPy SQLAlchemy AWS Azure SageMaker / Azure ML Preparation Tips 1. Build a RAG Project You Can Explain Don't just memorize definitions of RAG. Build or understand a practical pipeline: Documents → Chunking → Embeddings → Vector DB → Retrieval → Prompt → LLM → Response. Be prepared to explain why you selected a particular chunking strategy, embedding model and vector database. 2. Prepare LLM Fundamentals Revise: Transformers, Attention mechanism, BERT vs GPT-style architectures, Tokenization, Embeddings, Fine-tuning vs RAG, Hallucination, Context windows, Temperature, Evaluation metrics. A particularly strong answer should explain when RAG is preferable to fine-tuning. 3. Strengthen Python The JD specifically calls for expert-level Python. Practice writing clean production-style code rather than only ML notebooks. Prepare: OOP, Exception handling, Decorators, Generators, Async programming, API development, Data structures, Pandas/NumPy, SQLAlchemy. 4. Don't Ignore Data Engineering Expect questions around SQL, joins, indexing, schema design and ETL. Also understand how Spark distributes workloads and why PySpark is useful for large datasets. 5. Prepare an End-to-End Architecture Practice designing an AI application such as: FastAPI → Authentication → RAG Service → Vector DB → LLM → PostgreSQL → Monitoring. Then discuss scalability, latency, security, caching, data quality and failure handling. Expected Qualcomm Interview Rounds Round 1 – Recruiter/Initial Screening Round 2 – Coding/Programming Round 3 – ML/NLP Technical Round 4 – GenAI/Data Engineering Round 5 – System/Project Discussion Round 6 – Managerial/HR Expected Salary Range The job posting does not publish compensation. As a third-party reference, Indeed currently reports Qualcomm AI/ML Engineer compensation in India at approximately ₹8.75 lakh to ₹25 lakh annually, with an average of about ₹17.38 lakh/year based on five reported salaries. For this specialized 1–3 year role, a reasonable market expectation could be around ₹12–22 LPA, depending on existing compensation, technical depth, interview performance and the final internal level. This is an estimate, not an official Qualcomm salary range. Resume Tips for This JD Your resume should immediately communicate AI + production engineering capability. Instead of writing: "Worked on chatbot project." Use: "Developed a RAG-based conversational application using Python, LangChain and FAISS, improving retrieval relevance through optimized chunking and embedding strategies." Highlight: LLM projects, RAG implementations, NLP models, Python APIs, Vector databases, Spark/PySpark, SQL, Cloud deployment, Model performance, Data pipeline scale, Measurable improvements. Add GitHub/project links if they demonstrate genuine technical work. How to Apply Application Link: https://careers.qualcomm.com/careers/job/446718335666?domain=qualcomm.com&hl=en The role is AI/ML Engineer – Job ID 3090305, based in Chennai, Tamil Nadu. Location: Chennai Experience: 1–3 years Role: AI/ML Engineer Job ID: 3090305 Company: Qualcomm India Private Limited Education: Bachelor's degree in engineering, Information Systems, Computer Science or related field Qualcomm states that all worldwide openings are posted through its careers system and candidates should apply through the relevant job listing. Bottom Line: If you have 1–3 years of experience and want to move deeper into LLMs, RAG, NLP, ML engineering, data pipelines and production Python systems, this Qualcomm opportunity offers a strong combination of AI and software engineering. Prepare beyond theoretical ML—be ready to code, design, deploy and explain an AI system end to end.