Ai / Ml Engineer

Company: Qualcomm India Private Limited

Job Area: Engineering Group, Engineering Group > Software Engineering

Job Summary

General Summary: Experience and Qualifications: 5 to 10 years of experience applying AI and machine learning techniques to practical technology solutions. Expertise in ML, deep learning, TensorFlow, NLP, and Transformer architecture. Strong programming skills in Python, scripting, C/C++, with additional proficiency in Java as a complementary skill. Experience working with various target OSs like Android, Windows, Linux. Familiarity with deploying large language models (LLMs) and embedding model/sentence transformers in production use cases. Familiar with core machine learning model architecture and conventions, including CNNs, RNNs, Transformers, and ensemble methods. Thorough knowledge of basic algorithms, object-oriented design principles, and best practices. Ability to fine-tune large language models using custom content (documents, data, code). Ability to optimize Model inferences using techniques such as quantization, pruning, distillation while evaluating the runtime results on specific HW such as CPU, GPU, NPU etc. Proficiency in working with large-scale datasets, preprocessing, and creating appropriate data representations and applying concepts such as RAG, GGML to optimize results. Ability to keep up with newly released advances in AI/ML, toolsets, and architecture for both Edge and Cloud AI across multiple OSs and environments. Skilled in building and maintaining agentic workflows for autonomous AI systems, enabling goal-driven, adaptive behavior across complex tasks. Proficient in version control systems such as Git, with hands-on experience in collaborative workflows, branching strategies, and contributing to open-source projects

Responsibilities

• Fine-tune large language models using custom content (documents, data, etc.).
• Articulate and document solutions architecture and lessons learned for each exploration and accelerated incubation.
• Act as a liaison between stakeholders and project teams, delivering feedback and enabling necessary changes in product performance or presentation.
• Develop and optimize AI solutions on Qualcomm platforms, focusing on performance, efficiency, and real-world deployment constraints.
• Collaborate with startups, developers, and students, enabling them to understand and adopt emerging technologies through technical guidance, community engagement, and ecosystem support

Minimum Qualifications

• Bachelor's degree in Engineering, Information Systems, Computer Science, or related field and 3+ years of Software Engineering or related work experience.
• Master's degree in Engineering, Information Systems, Computer Science, or related field and 2+ years of Software Engineering or related work experience.
• PhD in Engineering, Information Systems, Computer Science, or related field and 1+ year of Software Engineering or related work experience.
• 2+ years of academic or work experience with programming languages such as C, C++, Java, Python, etc.

Preferred Qualifications

• Bachelor's or master's degree in computer science, Artificial Intelligence, Data Science, or a related field.
• Experience working on experimental/proto solutions and implementing end-to-end ML/NLP systems from development to deployment.
• Familiarity with relevant machine learning/deep learning frameworks such as PyTorch, TensorFlow.
• Exposure to transformer-based models (e.g., BERT, GPT, T5, Llama).
• Global team collaboration experience.
• Familiarity with cloud environments (GCP/AWS).
• Ability to document and create training solutions based on projects worked on.

Additional Elements

• Domain Expertise: Deep understanding of the specific industry or domain where AI/ML solutions will be applied.
• Knowledge of relevant regulations, data privacy, and ethical considerations.
• Collaboration and Communication Skills: Effective collaboration in cross-functional teams.
• Effective communication skills with non-technical audiences.
• Community collaboration through open-source contributions and knowledge sharing is a strong value-add skill.
• Prototyping and Rapid Iteration: Proficiency in rapid prototyping and iterative development.
• Problem-Solving and Creativity: Ability to tackle novel challenges and think creatively.
• Deployment and Scalability: Knowledge of deployment strategies and scalability considerations.
• Performance Optimization: Familiarity with optimizing model performance and resource usage.
• Continuous Learning: Commitment to stay updated with AI/ML advancements. Learning agility and adaptability in dynamic environments.
• Ability to balance multiple projects and priorities