AI Engineer

Company Description

Tensor Pilot, through its flagship product Tensor AI, provides a sophisticated desktop-based AI assistant for interacting with local files such as code, documents, images, and videos. Tensor AI emphasizes user privacy by processing and storing data locally, ensuring sensitive information remains on the user's device while leveraging cloud-based AI providers (OpenAI, Google, Anthropic) for intelligence. It delivers contextual insights specific to your workflows and files, surpassing the capabilities of generic cloud-based solutions. As part of the Tensor Pilot Suite, it also offers complementary tools like Drone AI, which automates drone operations, and Vision AI, which provides real-time video and image analysis.

Role Description

This is a full-time remote position for an AI Engineer specializing in intelligent document retrieval, agentic workflows, and advanced AI orchestration systems. The AI Engineer will be responsible for developing, optimizing, and deploying machine learning and AI models, with a focus on retrieval-augmented generation (RAG), vector databases, LangChain integration, and autonomous agent workflows. Tasks will include designing algorithms and workflows for intelligent file processing, implementing embedding systems, building multi-agent systems with tool calling capabilities, creating sophisticated AI pipelines with LangChain, and ensuring seamless integration into the Tensor Pilot Suite. Daily activities will involve software development, debugging, and collaborating with cross-functional teams to achieve key project milestones.

Qualifications

AI / ML Expertise : Proficiency in Pattern Recognition and Neural Networks, with understanding of machine learning algorithms and frameworks

Programming Skills : Strong background in Computer Science and Software Development, including experience with Python, Java, or C++

NLP & Language Models : Expertise in Natural Language Processing (NLP) and familiarity with modern language models

LangChain & AI Orchestration : Experience with LangChain framework, AI agent development, and complex workflow orchestration

Agentic Systems : Hands-on experience building autonomous agents, tool calling systems, and multi-agent workflows

Vector Database Experience : Proficiency with ChromaDB, Pinecone, or similar vector storage systems for semantic search

RAG Systems : Experience building retrieval-augmented generation systems with embedding models and document processing

AI Pipeline Development : Experience designing and implementing sophisticated AI workflows, chains, and agent-based systems

Problem Solving : Critical thinking, problem-solving, and the ability to design and optimize AI-driven solutions

Education : Bachelor's or Master's degree in Computer Science, Artificial Intelligence, or related field

Privacy-First AI : Experience with local data processing and privacy considerations in AI applications is a plus

Bonus : Experience with desktop applications, file system integration, local-first data architectures, and agent framework implementations

Perfect for AI engineers who want to work on cutting-edge retrieval systems, autonomous agents, and sophisticated AI orchestration while building privacy-focused, locally-processed AI solutions.