Senior Machine Learning Engineer- Work from Office

Responsibilities:

• 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.

Requirements:

• 4-8 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 (DPDPAct, GDPR, CCPA, ISO 27701).
• Exposure to Kubernetes, Docker, and ML deployment best practices.
• Contributions to open-source LLM projects or privacyAIresearch.

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