Machine Learning Engineer

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

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

What You’ll Do

* 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.

* 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.

* 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.

* 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).

* 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.

* 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.

What you’ll need [role requirements]:

Platform & Full-Stack Engineering

* API Design & Backend: Proven experience building robust, highly available RESTful APIs in Python (FastAPI preferred). Experience managing asynchronous workloads and task queues.

* Frontend Development: Solid experience building and maintaining modern, responsive web applications using React and TypeScript.

* Infrastructure & CI/CD: Comfortable working with Git, CI/CD pipelines, Docker, and Infrastructure as Code to deploy platform services reliably.

Data Engineering & MLOps

* Cloud Data Warehouses: Deep expertise in modern cloud data architectures, specifically Google Cloud Platform (BigQuery, GCS).

* Pipeline Orchestration: Hands-on experience with modern data orchestration and transformation tools (e.g., Apache Airflow, dbt) and familiarity with ML orchestrators (Kubeflow, Vertex AI).

* Familiarity with ML Workflows: You understand the data lifecycle of machine learning and know how to prepare data for training, inference, and evaluation.

* Vector Data: Experience or strong familiarity with processing unstructured data and interacting with Vector Databases for semantic search/RAG architectures.