Role Overview
We are seeking a highly skilled Applied AI Engineer to join our innovative team. In this role, you will be instrumental in bridging the gap between theoretical AI research and practical, high-impact business applications. You will design, develop, and deploy a range of machine learning models, from sophisticated recommendation systems and predictive analytics tools to fine-tuned generative AI solutions for enterprise challenges. Working with diverse datasets including text, images, and structured data, you will build robust, end-to-end AI pipelines. You will collaborate closely with product managers and data scientists to define problem spaces and deliver scalable, efficient, and cost-effective AI systems. This position requires a hands-on engineer passionate about leveraging the latest advancements in AI to solve real-world problems and drive measurable outcomes.
Requirements
- 3-5 years of professional experience in an AI, machine learning, or data science role with a focus on deployment.
- Strong proficiency in Python and associated ML libraries (e.g., scikit-learn, Pandas, NumPy).
- Hands-on experience with at least one major deep learning framework, such as TensorFlow or PyTorch.
- Demonstrable experience deploying and managing ML models on a major cloud platform (AWS SageMaker, Google AI Platform, Azure ML).
- Proven experience with LLM fine-tuning techniques (e.g., LoRA, full fine-tuning) and the Transformer architecture.
- Practical knowledge of building systems with vector databases (e.g., Pinecone, Milvus, Weaviate) and Retrieval-Augmented Generation (RAG).
- Experience designing and implementing end-to-end MLOps pipelines for model training, validation, and serving.
- Solid understanding of software engineering best practices, including version control, testing, and CI/CD.
Desirable
- Experience with containerization (Docker) and orchestration (Kubernetes) for deploying scalable services.
- Familiarity with data processing at scale using tools like Apache Spark or Dask.
- Contributions to open-source AI/ML projects or a portfolio of relevant personal projects.
- Experience with model optimization techniques such as quantization, pruning, or knowledge distillation.
- Strong communication skills with experience presenting complex technical concepts to non-technical stakeholders.