Machine Learning Engineer

Tech stack & ecosystem: (if applicable)

You will be the backbone of our ML platform, building everything from user-facing interfaces down to the data layers that feed our models:

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Platform & API: Python, FastAPI, React, TypeScript.

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Data Warehousing & Storage: Google Cloud Platform (GCP), BigQuery, Cloud Storage, Apache Parquet / Arrow.

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Data & ML Orchestration: dbt, Apache Airflow, Kubeflow Pipelines (KFP), Asynchronous Job Queues (Celery/RabbitMQ).

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Unstructured Data & GenAI: Vector Databases (e.g., Pinecone, Weaviate), modern RAG tooling (LangChain, LlamaIndex).

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Data Quality & Contracts: Pydantic, Great Expectations, strict JSON Schema validation.

What You’ll Do

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Own the API & Platform Layer: Design, build, and maintain the robust backend APIs (FastAPI) that serve as the bridge between our React frontend and our asynchronous, heavy-compute machine learning pipelines.

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Build the User Interface: Develop and maintain features in our React frontend, creating intuitive platform dashboards that allow users to design ML experiments, trigger data augmentation jobs, and visualize synthetic data metrics.

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Architect ML Data Pipelines: Build and maintain high-throughput ETL/ELT pipelines capable of ingesting massive tabular datasets directly into our Kubeflow training and inference workflows.

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Build the RAG Foundation: Develop the data pipelines that power our LLM digital twins. You will handle chunking, embedding generation, and vector indexing of unstructured text to enable highly accurate Retrieval-Augmented Generation (RAG).

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Optimize ML Data I/O: Optimize how our PyTorch models read and write data, leveraging columnar formats (Parquet) and distributed processing to eliminate I/O bottlenecks during training and generation.

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Enforce Strict Data Contracts: Ensure seamless communication between the frontend, backend, and ML workers by implementing strict data contracts (using Pydantic) and automated schema validation.