About the Role
We're hiring across the full experience spectrum for AI/ML Engineers — from engineers a few years into their career who are ready to own real production models, to senior practitioners who can architect ML systems end to end. You'll work on model development, training pipelines, and deployment for AI solutions used by enterprise clients across industries.
What You'll Do
- Design, train, and evaluate machine learning and deep learning models for real business problems — not academic benchmarks
- Build and maintain data pipelines and feature engineering workflows that feed model training and inference
- Take models from notebook to production — packaging, serving, and monitoring them once they're live
- Collaborate with data engineers, MLOps, and product teams to translate business requirements into ML solutions
- Debug model performance issues in production — drift, degradation, edge cases — and iterate quickly
- Stay current with the ML/AI landscape and bring in techniques (fine-tuning, RAG, classical ML) appropriate to the problem, not just what's trending
What We're Looking For
- 1–8 years of hands-on experience building and shipping machine learning models (range depends on role level — junior through senior candidates encouraged to apply)
- Strong Python skills and experience with ML frameworks such as PyTorch, TensorFlow, or scikit-learn
- Solid understanding of ML fundamentals — model evaluation, overfitting, feature engineering, and the trade-offs between approaches
- Experience working with structured and unstructured data at some scale
- Ability to communicate technical trade-offs clearly to both engineers and non-technical stakeholders
Nice to Have
- Experience with LLMs, fine-tuning, or RAG pipeline design
- Exposure to MLOps tooling (MLflow, Kubeflow, SageMaker, or similar) and CI/CD for ML
- Cloud platform experience (AWS, Azure, or GCP)
- Prior experience in a client-facing or consulting environment