AI Engineer

• We're looking for an AI Engineer who possesses a deep understanding of large-scale information retrieval systems, deep learning, databases, and RAG architectures. The ideal candidate will have expertise in developing and optimizing search algorithms, implementing efficient indexing techniques, and leveraging RAG to enhance AI-powered search and question-answering systems.

What You Will Be Doing

As an AI Engineer, you will play a pivotal role in designing, developing, and deploying advanced search and retrieval systems that leverage RAG techniques to solve complex information access challenges. You will collaborate with software engineers, customers, and business stakeholders to develop AI search solutions that deliver significant value to the organization and our clients.

Key Responsibilities

RAG System Research and Implementation: Lead the design and implementation of advanced retrieval systems like Deep Memory by Activeloop, delivering optimized RAG systems across the entire value chain - from embedding or model fine-tuning to retrieval optimization with custom algorithms, to enhance knowledge retrieval accuracy.

Search Algorithm Optimization: Develop and refine search algorithms, including semantic search, hybrid search, and multi-modal search techniques, to improve retrieval performance and relevance ranking.

Vector Database Integration: Implement and optimize vector storage and indexing solutions within Deep Lake, ensuring efficient similarity search capabilities for high-dimensional embeddings used in RAG systems.

Query Understanding and Processing: Design and implement advanced query processing pipelines, including query expansion, intent recognition, and contextual interpretation to enhance search precision.

Information Retrieval Model Development: Create and fine-tune machine learning models specifically for information retrieval tasks, such as document ranking, query-document relevance scoring, and zero-shot retrieval.

Performance Evaluation and Metrics: Establish comprehensive evaluation frameworks for search and RAG systems, including relevance assessments, A/B testing, and user satisfaction metrics to continually improve system performance.

Scalability and Efficiency: Optimize RAG and search systems for high throughput and low latency, ensuring they can handle large-scale datasets and real-time query processing demands.

Data Ingestion and Indexing: Develop efficient data ingestion pipelines and indexing strategies to support rapid updates and real-time search capabilities across diverse data types and sources.
What We Need to See

• Master's or PhD degree in Computer Science, Machine Learning, Statistics, or a related field.
• Strong programming skills in one or more programming languages, such as Python, or C++, and extensive experience with machine learning libraries, such as TensorFlow, PyTorch, Llama Index, LangChain etc.
• Proven experience in developing and deploying complex machine learning models in production environments, including experience with cloud-based platforms, edge devices, or embedded systems.
• Strong understanding of advanced machine learning algorithms, such as deep learning, reinforcement learning, RAGs, and ensemble methods, and experience with model optimization techniques, such as hyper-parameter tuning, model compression, and quantization.
• Solid understanding of data pre-processing, feature engineering, and data quality assurance techniques, and experience with large-scale, complex data sets.
• Excellent problem-solving skills and ability to analyze and interpret complex data sets to extract meaningful insights and drive decision-making.

Ways To Stand Out From The Crowd

• You have trained deep learning models in a distributed manner.
• You are a highly motivated, curious, hardworking explorer in the field of AI
• You have publications in top-tier machine learning and AI conferences such as ICML, NeurIPS, and CVPR (highly desirable).
• You have a builder attitude and you love building cool things that matter
• You're excited to work closely with the founding team in developing hyper-scalable software for ML
• You can proactively identify and anticipate problems and provide tangible solutions
• You enjoy the startup journey of building an endurable, scalable business