VTION - AI Engineer - Machine/Deep Learning

VTION seeks an AI Engineer to design, build, and deploy AI/ML solutions across its products and data platforms. Working with Product, Data Science, Engineering, and Business teams, the engineer will apply AI to data intelligence, automation, decision-making, and user experience, including applications built on consumer and behavioral data.

Responsibilities

• Build and integrate LLM-powered applications using models such as GPT, Claude, Gemini, or open-source alternatives. Develop RAG pipelines, semantic search, embeddings, and vector-based solutions.
• Develop NLP and text-intelligence capabilities for classification, summarization, sentiment analysis, entity extraction, and content understanding.
• Build and optimize pipelines for large-scale data processing and inference; adapt or fine-tune models for business use cases. Experiment with models, prompts, and architectures to improve accuracy, latency, and cost.
• Create APIs and services that integrate AI into existing products, move solutions from proof of concept to production, monitor performance and reliability, and establish frameworks for evaluating AI output quality.

Required qualifications

• 2–4 years of experience in AI/ML engineering, machine learning, data science, or a related field. The posting’s structured data specifies a bachelor’s degree.
• Strong Python programming; hands-on LLM and Generative AI application experience; knowledge of machine learning and deep learning fundamentals.
• Experience with frameworks such as PyTorch, TensorFlow, Hugging Face, LangChain, or LlamaIndex; NLP, embeddings, vector databases, and RAG architectures; REST APIs, databases, and data processing. Strong analytical and problem-solving skills are expected.

Preferred and additional qualifications:

• Cloud platform experience with AWS, GCP, or Azure is preferred. Familiarity with Docker, Git, CI/CD, and production deployment is a plus.
• Good to have: agentic workflows, LLM evaluation and observability, vector databases such as Pinecone, FAISS, Weaviate, Milvus, or Qdrant; fine-tuning, LoRA/PEFT, model optimization, MLOps, and experience with large-scale consumer, behavioral, or unstructured datasets or AI features for SaaS or consumer-tech products.

The role calls for independent ownership, rapid prototyping through production, and balancing accuracy, scalability, speed, cost, and user experience.