AI/ML & Data Engineer

Role Overview: You will be responsible for working with machine learning and natural language processing in document-heavy domains, particularly in finance, legal, and policy. Your main focus will be on data ingestion and preprocessing, NLP modeling, knowledge graph engineering, information retrieval, annotation and validation, MLOps, API integration, and more. Key Responsibilities: - Data Ingestion and Preprocessing: - Build and maintain data pipelines to ingest unstructured data from PDFs, gazettes, HTML circulars, etc. - Process data extraction, parsing, and normalization. - NLP & LLM Modeling: - Fine-tune or prompt-tune LLMs for summarization, classification, and change detection in regulations. - Develop embeddings for semantic similarity. - Knowledge Graph Engineering: - Design entity relationships (regulation, control, policy) and implement retrieval over Neo4j or similar graph DBs. - Information Retrieval (RAG): - Build RAG pipelines for natural language querying of regulations. - Annotation and Validation: - Annotate training data by collaborating with Subject Matter Experts (SMEs) and validate model outputs. - MLOps: - Build CI/CD for model retraining, versioning, and evaluation (precision, recall, BLEU, etc.). - API and Integration: - Expose ML models as REST APIs (FastAPI) for integration with product frontend. Qualification Required: - 4~6 years of experience in ML/NLP, preferably in document-heavy domains (finance, legal, policy). - Proficiency in Python and SQL. - Experience with Hugging face transformers, OpenAI API, Spacy, Scikit-Learn, LangChain, RAG, LLM prompt-tuning, LLM fine-tuning. - Familiarity with Pinecone, Weaviate, FAISS for Vector Search. - Knowledge of Airflow, Kafka, OCR (Tesseract, pdfminer) for Data Engineering. - Experience with MLflow, Docker for MLOps.,