Qualcomm Gen AI Engineer Fresh Graduates Freshers Hiring

About Company

We are Qualcomm’s Enterprise AI team, focused on accelerating AI adoption across the organization. Our team builds end-to-end AI solutions that empower engineering teams through intelligent automation and developer productivity. Our work spans a broad spectrum of AI engineering, including:

• Enterprise AI platforms and developer tools.
• AI agents and agentic workflows.
• Large Language Model (LLM) applications and post-training pipelines.
• Model deployment and inference at enterprise scale.
• Retrieval-Augmented Generation (RAG), knowledge systems, and AI infrastructure.
• Close collaboration with domain engineering teams to solve real-world problems using cutting-edge AI technologies.

As part of the team, you’ll have the opportunity to work on large-scale AI systems that are shaping the future of software engineering within Qualcomm.

Responsibilities

• Bachelor’s or Master’s degree in Computer Science or a closely related field from a reputed institution.
• Strong academic record, preferably with a CGPA/GPA of 8.5 or above.
• 0–1 year of professional experience (exceptional fresh graduates are encouraged to apply).
• Excellent fundamentals in core Computer Science subjects, including Data Structures & Algorithms, Operating Systems, Computer Networks, Database Management Systems, and Object-Oriented Programming.
• Strong problem-solving and analytical skills with the ability to write clean, efficient, and scalable code.
• Solid understanding of backend engineering principles and software development best practices.
• Passion for learning emerging AI technologies and building impactful engineering solutions.

Required Skills

• Programming: Python, Java (preferred), C++, or similar programming languages.
• Backend Development: REST APIs, FastAPI/Flask, microservices, distributed systems, and software design principles.
• Databases: SQL, NoSQL, data modeling, and vector databases.
• Data Engineering: ETL pipelines, data processing, and data management fundamentals.
• Artificial Intelligence & Machine Learning: Machine Learning fundamentals, Deep Learning basics, and practical experience with Generative AI.
• Large Language Models (LLMs): Prompt engineering, context engineering, Retrieval-Augmented Generation (RAG), AI agents, embeddings, and model orchestration frameworks.
• Cloud & DevOps (Preferred): Docker, Kubernetes, Git, CI/CD, and cloud platforms such as AWS, Azure, or GCP.
• Software Engineering: Strong debugging skills, system design fundamentals, testing, and version control using Git.