Machine Learning Ops Engineer

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. 3+ years of professional experience as a Software Engineer in AIML Prior experience or knowledge of working with machine learning & large language models Proficiency in programming languages such as Python, R, or Java. Knowledge of API development for serving generative models using Spring boot framework Experience with prompt engineering and vector embeddings. 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. Experience in maintaining data pipelines and working with streaming data processing frameworks like Apache Kafka, Flink, or Spark. Build end-to-end tooling for AI Development lifecycle within App Teams Pre-build components and reusable CI/CD templates Reduce time to AI-driven feature delivery Preferred Qualifications Knowledge of LLM model architectures - GPT, BERT Experience interfacing 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