Job Description
We are seeking a visionary Senior AI/LLM Engineer to lead our next-generation of autonomous systems. As we look toward the technological landscape of 2026 and beyond, we need a technical expert who can architect scalable models that redefine user interaction and solve complex real-world problems.
In this role, you will be at the forefront of Generative AI, working on proprietary models that will power our core product suite. You will bridge the gap between cutting-edge research and production-grade engineering, ensuring our AI solutions are not only powerful but also ethical, efficient, and secure.
Benefits & Perks:
- Competitive salary and equity package
- Comprehensive health, dental, and vision insurance
- Flexible remote/hybrid work options
- Continuous learning and development budget
- Access to the latest hardware for AI research
Responsibilities
- Model Development: Design, train, and fine-tune state-of-the-art Large Language Models (LLMs) and multimodal architectures using PyTorch and TensorFlow.
- Optimization: Optimize model inference latency and throughput for high-volume production environments using techniques like quantization, pruning, and distillation.
- Infrastructure: Build and maintain robust MLOps pipelines using Kubernetes, Docker, and cloud platforms (AWS/GCP) to ensure reliable model deployment.
- RAG Implementation: Implement Retrieval-Augmented Generation (RAG) strategies to enhance factual accuracy and reduce hallucinations in generated content.
- Cross-Functional Collaboration: Partner with product managers, data scientists, and backend engineers to translate business requirements into technical AI solutions.
- Evaluation: Establish rigorous testing frameworks and benchmarks to continuously monitor model performance and accuracy.
Qualifications
- Education: Master’s degree or PhD in Computer Science, Mathematics, or a related field (or equivalent professional experience in a top-tier AI organization).
- Experience: 5+ years of experience in Machine Learning, Deep Learning, or Natural Language Processing (NLP).
- Programming: Strong proficiency in Python and C++; familiarity with SQL and NoSQL databases.
- Frameworks: Deep experience with PyTorch, TensorFlow, Hugging Face Transformers, or LangChain.
- Cloud & DevOps: Experience deploying models to cloud environments and utilizing CI/CD pipelines.
- Problem Solving: Demonstrated ability to tackle complex algorithmic challenges and debug deep learning systems.