AI Engineer
About NeuroHire
NeuroHire is building an AI-first SaaS platform designed to make hiring smarter, faster, and more data-driven. AI is a core part of how the product works — powering candidate understanding, matching, automation, and decision-making.
We’re looking for an AI Engineer who can build real systems — not just models. Someone who can take an idea, turn it into a working pipeline, deploy it, and continuously improve it based on real usage.
If you enjoy solving practical problems with AI and shipping things that actually get used — you’ll fit right in.
What You’ll Work On
- Design and build AI systems that power core product features
- Develop and deploy machine learning and deep learning models
- Build LLM-based workflows using prompting, embeddings, and retrieval pipelines
- Work with unstructured data (text, resumes, job descriptions) to extract insights
- Create scalable inference systems optimized for performance and cost
- Integrate AI capabilities into backend services and APIs
- Monitor model performance and improve using real-world feedback
- Identify edge cases, bias, and failure scenarios early
- Contribute to the overall AI architecture as the platform scales
What We’re Looking For
- 3+ years of experience building AI/ML systems in production
- Strong foundation in machine learning and deep learning
- Proficiency in Python and frameworks like PyTorch, TensorFlow, or scikit-learn
- Experience with transformers, embeddings, and modern AI architectures
- Familiarity with LLMs and building AI-powered applications
- Experience deploying models in cloud environments (AWS, GCP, or Azure)
- Understanding of MLOps concepts such as model versioning and monitoring
- Ability to work with messy, real-world datasets
- Strong problem-solving mindset and ownership
Nice to Have (Not Required)
- Experience with generative AI or LLM-based applications
- Familiarity with vector databases or retrieval systems
- Experience optimizing inference pipelines
- Background in SaaS or product-based companies
- Knowledge of responsible AI or model explainability