Senior ML/MLOps Engineer

We are seeking a skilled ML/MLOps Engineer with expertise in building and optimizing data pipelines, deploying models, processing large datasets, and handling large-scale text data. The ideal candidate should have knowledge of ETL processes and experience deploying deep learning models on various platforms (e.g., Vertex real-time endpoints, Vertex batch processing, Nvidia Triton, ONNX, and Dataflow), along with familiarity with different batch processing workflows and building ML pipelines using Kubeflow.

Experience:

- Over 5 years in data engineering with a strong focus on building and managing data pipelines.

- Proven experience in deploying machine learning models and pipelines.

- Expertise in dataset preparation, curation, and quality management.

- Experience working with large-scale text data.

- Prior collaboration with modeling teams.

- Hands-on experience with Google Cloud Platform (GCP) services, including Vertex AI, BigQuery, Kubeflow, Dataflow, Cloud Storage, and other GCP services.

Required:

- Proficiency in Python and SQL.

- Experience with cloud AI platforms, e.g., Vertex AI.

- Familiarity with big data frameworks like Dataflow.

- Strong knowledge of ETL and data pipelines.

- Experience with workflow orchestration tools (e.g., Kubeflow, Airflow, Cloud Composer).

- Familiarity with databases (e.g., MySQL and MongoDB).

Nice to have:

- Knowledge of text processing libraries and NLP frameworks (e.g., NLTK, SpaCy, and Regex).

- Familiarity with concepts, developments, and tools in the LLM ecosystem (e.g., Instructor, Pydantic, Langchain, Embeddings, and Vector DBs).