Machine Learning Engineer

Job Description

Title: Machine Learning Engineer Location: Hyderabad/Trivandrum (India) Job Type: Contract Job Summary: Position Summary: We are seeking a dynamic Machine Learning Engineer focused on advancing our Predictive Modeling/Generative AI capabilities. You will contribute to building scalable AI systems that impact real world Enterprise applications, while promoting responsible AI practices and collaborating across teams to accelerate innovation. In this position you will: ? Design and implement machine learning models for anomaly detection in time series and behavioral data. ? Develop and maintain NLP pipelines for document processing and content generation. ? Preprocess and clean structured and unstructured data using standard techniques. ? Implement vectorization techniques and integrate with vector databases (e.g., FAISS, Pinecone, MongoDB Atlas Vector). ? Work with embedding models (e.g., OpenAI, Hugging Face) to support semantic search and retrieval tasks. ? Fine-tune and evaluate LLMs for specific use cases such as summarization, classification, and test case generation. ? Collaborate with backend engineers to expose ML models via APIs. ? Monitor model performance using metrics like precision, recall, F1 score, and ROC-AUC. ? Contribute to proof-of-concept projects involving GenAI and RAG architectures. ? Follow Responsible AI practices in model development and deployment. Required Qualifications: ? 4+ years of experience in machine learning or data science roles. ? Proficiency in Python and common ML/NLP libraries (e.g., scikit-learn, spaCy, Hugging Face Transformers). ? Hands-on experience with anomaly detection techniques such as Isolation Forest, One-Class SVM, Autoencoders, or statistical methods is a plus ? Familiarity with NLP tasks such as classification, summarization, and named entity recognition. ? Experience with vectorization techniques (TF-IDF, Word2Vec, BERT, etc.). ? Experience with vector databases (e.g., FAISS, Pinecone, ChromaDB) is a plus ? Exposure to LLMs and prompt engineering. ? Basic understanding of MLOps concepts and model deployment practices.