Senior Machine Learning Engineer

Job Description

Experience : 3+ years Shift : (GMT+05:30) Asia/Kolkata (IST) Must have Skills required: NLP, LLM, Python We are looking for a Senior Machine Learning Engineer with expertise in LLMs, NLP, and privacy-focused AI solutions to join our Mumbai team and help shape the future of AI-powered data protection. In this role you will: Develop and fine-tune LLMs for contract analysis, regulatory classification, and risk assessment. Implement Retrieval-Augmented Generation (RAG) using vector embeddings and hybrid DB-based querying to power DPIA and compliance workflows. Build AI-driven contract analysis systems to detect dark patterns, classify clauses, and provide remediation suggestions. Develop knowledge graph-based purpose taxonomies for privacy policies and PII classification. Automate data discovery for structured and unstructured data, classifying it into PII categories. Optimize sliding window chunking, token-efficient parsing, and context-aware summarization for legal and compliance texts. Build APIs and ML services for deploying models in a high-availability production environment. Collaborate with privacy, legal, and compliance teams to build AI solutions that power Privys data governance tools. Stay ahead of the curve with agentic RAG, multi-modal LLMs, and self-improving models in the compliance domain. Must-Have Skills 3-5 years of experience in Machine Learning, NLP, and LLM-based solutions. Strong expertise in fine-tuning and deploying LLMs (GPT-4, Llama, Mistral, or custom models). Experience with RAG-based architectures, including vector embeddings (FAISS, ChromaDB, Weaviate, Pinecone, or similar). Hands-on with agentic RAG, sliding window chunking, and efficient context retrieval techniques. Deep understanding of privacy AI use cases, including contract analysis, regulatory classification, and PII mapping. Proficiency in Python and frameworks like PyTorch, TensorFlow, JAX, Hugging Face, or LangChain. Experience in building scalable AI APIs and microservices. Exposure to MLOps practices, including model monitoring, inference optimization, and API scalability. Experience working with at least one cloud provider (AWS, GCP, or Azure). Good-to-Have Skills Experience in hybrid AI architectures combining vector search+ relational databases. Familiarity with functional programming languages (Go, Elixir, Rust, etc.). Understanding of privacy compliance frameworks (DPDP Act, GDPR, CCPA, ISO 27701). Exposure to Kubernetes, Docker, and ML deployment best practices. Contributions to open-source LLM projects or privacy AI research. Skills NLP, LLM, Python