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 Privy s data governance tools.
- Stay ahead of the curve with agentic RAG, multi-modal LLMs, and self-improving models in the compliance domain.
Skills Required:
LLM , RAG , AgenticAI , NLP , Python , Problem solving
Candidate Attributes:
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.
