Congruent InfoTech

Experience : 4~6 years in ML/NLP, preferably in document-heavy domains (finance, legal, policy) Key Responsibilities: Data Ingestion and Preprocessing : Ability to build and maintain data pipelines to ingest unstructured data from PDFs, gazettes, HTML circulars etc. and process data extraction, parsing, and normalization NLP & LLM Modeling : Ability to fine-tune or prompt-tune LLMs for summarization, classification, and change detection in regulations. Ability to develop embeddings for semantic similarity. Knowledge Graph Engineering : Ability to design entity relationships (regulation, control, policy) and implement retrieval over Neo4j or similar graph DBs. Information Retrieval (RAG) : Ability to build RAG pipelines for natural language querying of regulations. Annotation and Validation : Ability to annotate training data by collaborating with SMEs and validate model outputs MLOps : Ability to build CI/CD for model retraining, versioning, and evaluation (precision, recall, BLEU, etc.) API and Integration : Ability to expose ML models as REST APIs (FastAPI) for integration with product frontend. Skills : Languages : Python, SQL AI/ML/NLP : Hugging face transformers, OpenAI API, Spacy, Scikit-Learn, LangChain, RAG, LLM prompt-tuning, LLM fine-tuning Vector Search : Pinecone, Weaviate, FAISS Data Engineering : Airflow, Kafka, OCR (Tesseract, pdfminer) MLOps : MLflow, Docker