About the Role
- - - - What the Candidate Will Do ----
- Solve business-critical problems using a mix of generative AI, classical ML, and deep learning.
- Build generative AI applications (e.g., conversational assistants, text summarization, multimodal experiences) using large language models and related architectures.
- Collaborate with product, science, and engineering teams to execute on the technical vision and roadmap for Applied AI initiatives.
- Deliver high-quality, production-ready ML systems and infrastructure, from experimentation through deployment and monitoring.
- Adopt best practices in ML development lifecycle (e.g., data versioning, model training, evaluation, monitoring, responsible AI).
- Deliver enduring value in the form of software and model artifacts.
- - - - Basic Qualifications ----
- Master or PhD or equivalent experience in Computer Science, Engineering, Mathematics or a related field and 2+ years of Software Engineering work experience.
- Experience in programming with a language such as Python, C, C++, Java, or Go.
- Experience with ML packages such as Tensorflow, PyTorch, JAX, and Scikit-Learn.
- Experience with SQL and database systems such as Hive, Kafka, and Cassandra.
- Experience in the development, training, productionization and monitoring of ML solutions at scale.
- Strong desire for continuous learning and professional growth, coupled with a commitment to developing best-in-class systems.
- Excellent problem-solving and analytical abilities.
- Proven ability to collaborate effectively as a team player.
- - - - Preferred Qualifications ----
- Prior experience working with generative AI (e.g., LLMs, diffusion models) or multimodal AI and integrating such technologies into end-user products.
- Exposure to audio ML and voice AI (STT, TTS, voice embeddings, etc.).
- Experience in modern deep learning architectures and probabilistic models.
- Machine Learning, Computer Science, Statistics, or a related field with research or applied focus on large-scale ML systems.