Junior Machine Learning Engineer

Assist in designing, building, and fine-tuning machine learning models for document classification, entity extraction, and outcome prediction using tools like scikit-learn, PyTorch, or TensorFlow.

Support internal departments by transforming legacy workflows through the integration of AI-powered tools.

Evaluate Performance: Help establish metrics like recall, precision, and accuracy to assess model performance, optimizing to reduce errors in tax form extraction and chatbot responses.

Deploy & Monitor: Support model deployment using tools like MLflow, FastAPI, or Streamlit, and assist in versioning, logging, and monitoring live systems.

Learn & Grow: Contribute to a dynamic team, gaining hands-on experience with real-world AI applications in tax automation.

Education & Experience: Recent graduate (0–2 years of experience, including internships) in AI, NLP, computer science, or related fields.

Technical Skills:

Basic proficiency in Python and familiarity with libraries like scikit-learn, PyTorch, or TensorFlow; familiarity with pandas for document extraction, a PDF extraction library, and/or token estimation libraries is a plus.

Experience working with LLM APIs and their payload outputs (e.g., OpenAI, Azure AI), along with OCR/document intelligence tools (e.g., Azure Document Intelligence, Google Document AI).

Interest in working with structured/unstructured data, especially financial or tax documents.

Soft Skills:

Exceptional communication skills to bridge technical and non-technical teams.

Eager to learn, with a proactive approach to problem-solving.

Strong documentation habits and a team-oriented mindset.

Nice-to-Have