Job description
Project Role : AI / ML Engineer
Project Role Description : Develops applications and systems that utilize AI tools, Cloud AI services, with proper cloud or on-prem application pipeline with production ready quality. Be able to apply GenAI models as part of the solution. Could also include but not limited to deep learning, neural networks, chatbots, image processing.
Must have skills : Machine Learning (ML)
Good to have skills : Microsoft Azure Machine Learning
Minimum 3 year(s) of experience is required
Educational Qualification : 15 years full time education
Summary
ML Engineer : Build features across Agentic, LLM and ML workflows including suggestion/rules components, search & retrieval, document extraction, and basic image/OCR processing. Translate problem statements into production-ready code, write clear documentation, and partner closely with MLOps for reliable releases. Should be aware of Model Drift & Data Drift practices.
Roles and responsibilities:
Write queries and extract data from structured/unstructured sources implement parsing and normalization pipelines.
Develop web/document extraction (Playwright/Selenium, Trafilatura pypdf/pdfplumber/ocrmypdf) and convert to validated schemas.
Implement prompts, tools/functions, and agent steps using LangChain contribute to retrieval (BM25 + embeddings) and RAG modules.
Add basic image processing with OpenCV and OCR using pytesseract where needed.
Write clean, tested Python add unit-style LLM tests with DeepEval maintain experiment logs and evaluation datasets.
Collaborate in Agile ceremonies produce concise design notes and experiment reports.
Technical experience & Professional attributes:
Python with hands-on PyTorch familiarity with deep-learning packages and the Hugging Face stack (transformers, datasets, SBERT).
Web automation/scraping using Selenium or Playwright robust HTML/text processing.
Search basics and RAG patterns vector stores and embeddings at a practical level.
Image processing fundamentals (OpenCV) and OCR integration (pytesseract).
Evaluation mindset: DeepEval for LLM outputs Optuna/SHAP exposure is a plus.
Preferred Skills
spaCy, scikit-learn LightGBM/Flair where relevant.
Experience with schema validation (pydantic/JSON Schema) and tokenization (tiktoken).
Streamlit for internal demos (local-only).
Education qualifications:
Experience shipping ML/LLM features or strong applied projects demonstrating end-to-end ownership.
Clear written/spoken communication and collaborative ways of working.
You will be working with a Trusted Tax Technology Leader, committed to delivering reliable and innovative solutions
This job post has been translated by AI and may contain minor differences or errors.
