Qualcomm Recruitment 2026 | AI, Machine Learning & Data Science Engineering Lead | Experienced Professionals
Job Description
Eligibility Criteria
Top engineering graduates holding a Bachelor’s, Master’s, or PhD in Computer Science, Information Systems, or a closely related technical discipline.
The role demands 4 to 15 years of robust industry experience specifically targeting high-level Data Science Engineering Roles and complex backend architecture.
Candidates must demonstrate a minimum of 2+ years of hands-on, production-level expertise in core programming languages such as Python, C++, or Java.
Applicants should possess a deep understanding of cloud architecture careers and have proven experience deploying models within major cloud ecosystems like AWS or Azure.
Strong analytical reasoning, problem-solving skills, and the capacity to take autonomous ownership of large-scale technical projects are strictly required.
Job Responsibilities & Core Skills Required
Implement Advanced Machine Learning Algorithms: Design, fine-tune, and deploy sophisticated ML and NLP models by leveraging industry-standard frameworks like PyTorch, TensorFlow, and Scikit-learn.
Architect LLM & RAG Frameworks: Drive AI innovation by integrating Large Language Models (LLMs), Transformers, and Retrieval-Augmented Generation (RAG) pipelines utilizing LangChain, LlamaIndex, and FAISS.
Optimize Cloud Infrastructure: Work seamlessly within advanced cloud computing environments, deploying production-grade registries and intelligent systems on AWS Sagemaker and Azure ML.
Develop Scalable Enterprise Applications: Build robust Python backend services and highly secure APIs using modern frameworks like FastAPI, Django, and Flask to support complex data workflows.
Master Enterprise Data Engineering: Design highly efficient ETL data ingestion pipelines, governing big data ecosystems via PySpark, Hadoop, PostgreSQL, and high-performance NoSQL databases.
Cross-Functional Technical Leadership: Take complete end-to-end ownership of transformative ML solutions, collaborating closely with system architects and hardware engineering teams to translate business requirements into technical reality.
Application Process Overview
Securing a leadership position within prestigious Machine Learning Engineering Roles at Qualcomm requires a highly structured and prepared approach to the corporate screening process. Begin by optimizing your resume to clearly highlight your proficiency in Python backend development, NLP frameworks, and scalable data pipeline architecture.
The technical evaluation process typically features multiple rigorous rounds, focusing heavily on algorithm optimization, complex system design, and advanced cloud integration capabilities. You can expect in-depth discussions regarding your past experience with data governance and large language model fine-tuning.
To officially submit your candidacy for this role, please scroll down to the bottom of this page. Click the "Apply Now" button provided in the dedicated section below to route your application securely and directly to the Qualcomm hiring portal.
Conclusion
Stepping into an engineering leadership role at Qualcomm offers an unparalleled professional trajectory for those passionate about building the future of connected technology. Polish your technical portfolio, refine your advanced algorithmic skills, and apply today to take your career to the next level.
Frequently Asked Questions (FAQs)
1. What are the core skills required for the Qualcomm AI & Data Science Engineering Lead role? Ideal candidates must possess expert-level Python programming skills and extensive experience with ML/NLP frameworks like PyTorch and TensorFlow. A strong background in building RAG pipelines and deploying models on cloud platforms like AWS or Azure is also crucial for success.
2. How much experience is needed to apply for this Machine Learning job in Chennai? Qualcomm is targeting seasoned professionals with a proven track record. Applicants should have anywhere from 4 to 15 years of relevant industry experience, scaling according to their specific degree (Bachelor's, Master's, or PhD).
3. Does this role involve backend software development alongside data science? Yes, this position heavily requires the ability to develop robust, production-grade backend components. Engineers will utilize Python frameworks such as FastAPI or Django to seamlessly integrate machine learning solutions into live enterprise environments.