Job description
As a Software Engineer you will be responsible in building the next generation of intelligent systems for Apple s corporate applications. You will work in a dynamic environment to take an ideation to implementation in a multi-functional environment. The ideal candidate will be self-motivated, pro-active and solution oriented. Attention to detail and dedication to providing a high-quality, stable delivery are essential.You will be challenged to find creative solutions to technical problems, feel comfortable working with sophisticated systems and large data sets, collaborating in a team environment. We promote innovation to improve our product performance and stay deeply focused on delighting our users.
- Bachelor s degree in Computer Science or equivalent work experience.
- 4+ years of professional experience as a Software Engineer in AI or ML Platforms
- 4+ years of professional experience API development for serving generative models using Java, Springboot framework
- Prior experience of working with machine learning & LLMs
- 4+ years of professional experience in developing data pipelines and working with streaming data processing frameworks like Apache Kafka, Flink, or Spark.
Preferred Qualifications
- Proficiency in programming languages such as Java, Python or R
- Knowledge of data engineering principles, including data preprocessing, ETL processes, and tools
- Proven track record of working on AI/ML projects from conception to deployment.
- Prior experience with prompt engineering and vector embeddings.
- Knowledge of LLM model architectures - GPT, BERT
- Experience collaborating with ML Platform team utilising and integrating ML APIs with applications
- Familiarity with ML libraries and frameworks, including TensorFlow, PyTorch, Pandas, and Scikit-learn.
- Understanding of RAG and graph-based RAG methodologies.
- Skills in rapid prototyping and experimentation with generative models
- Experience in performance optimisation, particularly in enhancing model performance and inference speed for real-time applications+ Understanding of RAG and graph-based RAG methodologies.
- Skills in rapid prototyping and experimentation with generative models
- Experience in performance optimisation, particularly in enhancing model performance and inference speed for real-time applications
