AI Engineer
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Summary
An AI Engineer at Jobberman owns the full lifecycle of AI features — defining problems, preparing data, and building, fine-tuning, and deploying ML/deep learning models (NLP, computer vision, predictive analytics) plus LLM apps like chatbots, RAG platforms, and AI agents, served via containerized, cloud-hosted APIs.
Summary
We are seeking a dedicated AI Engineer to spearhead the development and implementation of intelligent capabilities within our product ecosystem. This position offers the opportunity to engage in the complete lifecycle of AI solutions, ranging from initial problem definition and data preparation to model construction, large language model integration, and reliable production deployment. The ideal candidate will balance a strong commitment to technical precision with a practical focus on delivering tangible business value. This role is pivotal for bridging the gap between advanced artificial intelligence and real-world user needs, ensuring that our technological innovations translate into effective, scalable solutions.
Responsibilities
- Design, train, fine-tune, and assess machine learning and deep learning architectures for diverse applications, including natural language processing, computer vision, predictive analytics, and classification tasks.
- Develop and deploy applications leveraging large language models, such as conversational agents, retrieval-augmented generation (RAG) platforms, autonomous AI agents, and streamlined automated processes.
- Seamlessly integrate third-party AI APIs and open-source model repositories into existing product frameworks to enhance functionality and performance.
- Manage the end-to-end data lifecycle, including the collection, cleansing, labeling, and pipeline construction required for both model training and real-time inference.
- Engineer robust deployment pipelines for models using containerization and cloud infrastructure, ensuring high availability and scalability via API services.
- Continuously monitor deployed systems for performance metrics, operational costs, and latency, implementing iterative improvements to maintain optimal efficiency.
- Collaborate cross-functionally with product management, engineering, and design teams to pinpoint high-impact opportunities where AI can address specific user challenges.
- Maintain comprehensive documentation regarding experimental methodologies, architectural decisions, and model performance outcomes.