Senior AI Engineer

Job Description: Senior AI Engineer – NLP & GenAI

Company: HyperVerge

Experience Level: 3–5 Years

Location: Bengaluru, India (Hybrid / On-site)

About HyperVergeHyperVerge is a B2B AI company powering AI-driven identity verification, document intelligence, fraud prevention, and financial onboarding for top global institutions. We process hundreds of millions of checks annually, delivering low-latency, production-ready AI models deployed at global scale.

Role OverviewWe are looking for a Senior AI Engineer with core expertise in Natural Language Processing (NLP) and Generative AI to build, fine-tune, and deploy enterprise-grade language models. In this role, you will lead the end-to-end development of unstructured data solutions, automated document parsing, entity extraction, and conversational agents used in financial onboarding and risk intelligence.

Key ResponsibilitiesArchitecture & Model Development: Design, train, and fine-tune NLP models, Large Language Models (LLMs), and domain-specific Transformer architectures (BERT, RoBERTa, LLaMA, Mistral) for tasks like text extraction, entity recognition (NER), summarization, and document parsing. RAG & Agentic Systems: Architect high-throughput Retrieval-Augmented Generation (RAG) pipelines, semantic search engines, and agentic workflows using vector databases (Milvus, Qdrant, Pinecone). Production Deployment & MLOps: Deploy deep learning models into scalable APIs/SDKs on cloud infrastructure (AWS/GCP), ensuring low latency, high throughput, and efficient hardware utilization (PyTorch, TensorRT, vLLM).Data Strategy & Annotation: Oversee data pipeline development, active learning strategies, and efficient dataset curation/annotation loops for complex NLP tasks.Cross-Functional Collaboration: Partner closely with Backend Engineers, Product Managers, and Client Success teams to integrate core AI engine capabilities into customer-facing products.

Key Requirements & Qualifications1. Core Technical SkillsExperience: 3 to 5 years of hands-on experience building and deploying machine learning and NLP models in a production environment.NLP & GenAI Expertise: Deep understanding of classical NLP techniques (TF-IDF, SpaCy, NLTK) alongside modern Generative AI techniques, fine-tuning LLMs (LoRA, QLoRA, SFT), prompt engineering, and guardrails.Deep Learning Frameworks: Proficient in Python, PyTorch, Hugging Face Transformers, and LangChain/LlamaIndex.Vector DBs & Search: Strong experience with vector indexing, embeddings, and hybrid search pipelines.MLOps & Infrastructure: Solid background in Docker, CI/CD, ONNX, TensorRT, Triton Inference Server, and cloud platforms (AWS / GCP).2. Preferred & Bonus QualificationsExperience with Multimodal AI (combining document OCR/Computer Vision with NLP).Prior exposure to fintech, fraud detection, or document intelligence domains.B.Tech / M.Tech in Computer Science, Electrical Engineering, Data Science, or a related quantitative field.

Core Competencies & CultureAnalytical Problem Solving: Ability to translate complex, messy business problems into crisp machine learning formulations.Ownership & Drive: High bias for action and speed in an agile, startup-paced environment.Adaptability: Willingness to move across the stack—from model research to production MLOps.